Predictive Maintenance for Industrial Machines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Maintenance for Industrial Machines using IoT Data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Overview:\n",
    "The project aims to develop a system that uses machine learning to predict when an industrial machine is likely to fail, using real-time data captured through IoT sensors. This predictive maintenance system will help companies save on repair costs and downtime by addressing potential issues before they lead to machine failures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1: Foundations of Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1.1: Understand the principles of data science and its applications in industrial contexts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Research Goals:\n",
    "\n",
    "a. Understand data science fundamentals: data collection, preprocessing, analysis, and interpretation.\n",
    "\n",
    "b. Explore industrial applications of data science, such as predictive maintenance and process optimization.\n",
    "\n",
    "c. Identify challenges and best practices for applying data science in industrial settings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings:\n",
    "\n",
    "a. Data Science Fundamentals:\n",
    "\n",
    "* Data Collection: Gathering relevant data from various sources\n",
    "* Preprocessing: Cleaning, transforming, and organizing data for analysis.\n",
    "* Analysis and Interpretation: Extracting insights and patterns from data.\n",
    "\n",
    "b. Industrial Applications:\n",
    "\n",
    "* Predictive Maintainence: Using data to predict machine failures and optimize maintainence schedules.\n",
    "* Process Optimization: Enhancing efficiency and quality by analyzing production processes.\n",
    "\n",
    "c. Challenges and Best Practices:\n",
    "\n",
    "* Data Quality: Ensuring accurate and reliable data.\n",
    "* Interdisciplinary Collaboration: Combining domain expertise with data science skills.\n",
    "* Ethical Considerations: Balancing data-driven decisions with ethical implications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1.2: Review statistical methods and perform exploratory data analysis on historical machine data to identify patterns and anomalies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We'll use Python with Pandas and Matplotlib libraries to perform exploratory data analysis on the historical machine data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the necessary libraries for this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the historical machine data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Machine Predictive Maintenance Classification\\predictive_maintenance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UDI</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Type</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>Target</th>\n",
       "      <th>Failure Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>M14860</td>\n",
       "      <td>M</td>\n",
       "      <td>298.1</td>\n",
       "      <td>308.6</td>\n",
       "      <td>1551</td>\n",
       "      <td>42.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>L47181</td>\n",
       "      <td>L</td>\n",
       "      <td>298.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>1408</td>\n",
       "      <td>46.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>L47182</td>\n",
       "      <td>L</td>\n",
       "      <td>298.1</td>\n",
       "      <td>308.5</td>\n",
       "      <td>1498</td>\n",
       "      <td>49.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>L47183</td>\n",
       "      <td>L</td>\n",
       "      <td>298.2</td>\n",
       "      <td>308.6</td>\n",
       "      <td>1433</td>\n",
       "      <td>39.5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>L47184</td>\n",
       "      <td>L</td>\n",
       "      <td>298.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>1408</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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]  Target Failure Type  \n",
       "0                    1551         42.8                0       0   No Failure  \n",
       "1                    1408         46.3                3       0   No Failure  \n",
       "2                    1498         49.4                5       0   No Failure  \n",
       "3                    1433         39.5                7       0   No Failure  \n",
       "4                    1408         40.0                9       0   No Failure  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               UDI  Air temperature [K]  Process temperature [K]  \\\n",
      "count  10000.00000         10000.000000             10000.000000   \n",
      "mean    5000.50000           300.004930               310.005560   \n",
      "std     2886.89568             2.000259                 1.483734   \n",
      "min        1.00000           295.300000               305.700000   \n",
      "25%     2500.75000           298.300000               308.800000   \n",
      "50%     5000.50000           300.100000               310.100000   \n",
      "75%     7500.25000           301.500000               311.100000   \n",
      "max    10000.00000           304.500000               313.800000   \n",
      "\n",
      "       Rotational speed [rpm]   Torque [Nm]  Tool wear [min]        Target  \n",
      "count            10000.000000  10000.000000     10000.000000  10000.000000  \n",
      "mean              1538.776100     39.986910       107.951000      0.033900  \n",
      "std                179.284096      9.968934        63.654147      0.180981  \n",
      "min               1168.000000      3.800000         0.000000      0.000000  \n",
      "25%               1423.000000     33.200000        53.000000      0.000000  \n",
      "50%               1503.000000     40.100000       108.000000      0.000000  \n",
      "75%               1612.000000     46.800000       162.000000      0.000000  \n",
      "max               2886.000000     76.600000       253.000000      1.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values:\n",
      " 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",
      "Target                     0\n",
      "Failure Type               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "missing_values = data.isnull().sum()\n",
    "print(\"Missing Values:\\n\", missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot histogram for numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(10, 8))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select only numeric columns for correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_data = data.select_dtypes(include='number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_matrix = numeric_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot heatmap of correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.title('Correlation Matrix')\n",
    "plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='nearest')\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(correlation_matrix.columns)), correlation_matrix.columns, rotation=45)\n",
    "plt.yticks(range(len(correlation_matrix.columns)), correlation_matrix.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plot for selected numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P3NxcjBw5Uik2KysrdOjQASdOnFDZ5j//+Y9SPE2bNoWjoyOGDRumWN60aVO4uLiovYN04sSJSqMZU6ZMgbW1teJ11SWmKVOmqCx7+fd/UVERnj59ik6dOoExhsuXL2v4Cmnn1Th27doFkUiE0NBQpXMJDg6Gk5OT2nPhCl3GMrJ27dph7969kEgkuHLlCvbt24dly5Zh6NChSElJQWBgIO7cuQM+n//aL8lr167hiy++wPHjx5Gfn6/0XF5ens4xyn/RREVFVbpOXl4eXF1dFY9fHcp9nblz56Jr166wsrJC7dq1ERAQUGWB7/379wFU/JJ5VUBAAOLi4vReMLdz506UlZWhTZs2uH37tmJ5hw4d8NNPP2Hq1Kmv3Ye2r4s6t27dwl9//aWSLMi9Wtzu4+Oj9Fie+Hh7e6td/vz5c6Xl/v7+Kj2DmjRpAqCi/1HdunW1jqmy12HLli3473//ixs3bqCsrOy161fXq/u9ffs2GGOYM2cO5syZo3ab7OxseHp6qn3O2toaQ4YMwfbt21FaWgpbW1vs3bsXZWVlSsnOrFmzcPToUbRv3x7+/v7o06cPRo0ahc6dO+t8Lh06dMBXX30FHo8HBwcHBAQEqNz4oO6cNf1ZKiwsRH5+/msvuWv6WXjvvffwyy+/ICIiAp6enujTpw+GDRuG8PBwxbpffvklBg4ciCZNmqBFixYIDw/HmDFjdL5sKf9dFhISovZ5oVCo9NjOzk7lPEQiEby8vFR+JkQikcrPDgA0btxY6bGTkxPq1aunuFNM25isra3h5eWlsl5GRgbmzp2L33//XSWO6vz+r4y6OG7duoW8vDx4eHio3caUbryhZIcjAoEA7dq1Q7t27dCkSROMGzcOu3btwrx58zTaPjc3F927d4dQKMSXX36JRo0awc7ODpcuXcKsWbNURl60Id926dKlaN26tdp1nJyclB6//FeGJlq2bInevXvrFJ+xyG8zr+wL6e7du2jYsGGV+9D2dVFHJpMhNDQUn376qdrn5YmInJWVldr1KlvOdKix0jYmda/Djz/+iLFjx2LQoEH45JNP4OHhASsrK8TExChquF6Hx+OpjV8qlapd/9U45J/1jz/+GGFhYWq38ff3rzKGESNGYO3atTh06BAGDRqEX375Bc2aNUOrVq0U6wQEBODmzZv4448/cPjwYezZswerV6/G3Llzdb4tuHbt2hr9DOnjM1gVTT8LHh4eSElJQVxcHA4dOoRDhw5h06ZNiIyMxJYtWwBU1N/cuXMHv/32G44cOYINGzZg2bJlWLNmjdKIizaxARU1MnXr1lV5/tU/sIz1s6NNTLa2tioj/FKpFKGhocjJycGsWbPQrFkzODo64uHDhxg7dqxGv/8ra4Ja2c+OujhkMhk8PDwqbclRWQLMBUp2TIB8iDgzMxMA0KhRI8hkMqSlpVWabCQkJODZs2fYu3cvunXrplienp6usm5lH+rKljdq1AhAxV8YppKQ+Pr6AgBu3ryp8tyNGzdQu3ZtxaiOPjoZp6en48yZM5g2bRq6d++u9JxMJsOYMWOwfft2fPHFF9U+llxV70dhYaHR3gv5aMfL8fz9998AoCic1EdMu3fvRsOGDbF3716lY72a8Ff1frq6uqq9lCAfvXgdebJqY2Oj87l069YN9erVw86dO9GlSxccP35cUWT+MkdHRwwfPhzDhw+HRCLB4MGDsWjRIkRHRxu1HYGmP0v29vYQCoW4evVqlfvT5rMgEAjQv39/9O/fHzKZDO+99x7Wrl2LOXPmKJJKNzc3jBs3DuPGjUNhYSG6deuG+fPnV5nsvO53mYeHh9F+fm7duoWePXsqHhcWFiIzMxN9+/bVW0ypqan4+++/sWXLFkRGRiqWq7tEWNlrIx+Zf7UfmaY/O0DFuRw9ehSdO3c2eFJdXVSzY0QnTpxQ+5eA/FqufFh50KBB4PP5+PLLL1UydPn28r80Xt6fRCLB6tWrVfbv6OiodlhTnhy8+mEPDg5Go0aN8O2336KwsFBluydPnlR6joZSr149tG7dGlu2bFGK9+rVqzhy5IjiFwlQ+XkBwNOnT3Hjxg0UFxdXeTz5Xyqffvophg4dqvRv2LBh6N69e6V/zejK0dFRbczDhg1DUlIS4uLiVJ7Lzc1FeXm5XuN49OiRUife/Px8bN26Fa1bt1b8JaqPmNR9hs+dO4ekpCSl9eR3fah7bRo1aoQbN24ofSavXLmC06dPv/b4QMUXTo8ePbB27VrFHxsv0+SzzufzMXToUOzfvx/btm1DeXm50iUsoKL+62UCgQCBgYFgjCku38lr7Z4+fapR7LrS9GeJz+dj0KBB2L9/Py5cuKCyH/n7puln4dXXgM/nKy5PlZaWql3HyckJ/v7+iucrU9nPfFhYGIRCIRYvXqx0mVTOEL/L1q1bp3Ss2NhYlJeXIyIiQm8xqfvZYYypbU5b2Wvj6+sLKysrlfo6dd8hlRk2bBikUikWLlyo8lx5ebneGrvqA43sGNH06dNRXFyMt956C82aNYNEIsGZM2ewc+dO+Pn5Ydy4cQAqhs0///xzLFy4EF27dsXgwYNha2uL5ORk1K9fHzExMejUqRNcXV0RFRWF999/HzweD9u2bVObTAUHB2Pnzp2YOXMm2rVrBycnJ/Tv3x+NGjWCi4sL1qxZA2dnZzg6OqJDhw5o0KABNmzYgIiICDRv3hzjxo2Dp6cnHj58iBMnTkAoFGL//v3GfvmwdOlSREREQCwWY/z48YrbZUUikVI79ODgYAAVt92PGDECNjY26N+/PxwdHTW+9fynn35C69atVepc5AYMGIDp06fj0qVLaNu2rV7OLzg4GLGxsfjqq6/g7+8PDw8PhISE4JNPPsHvv/+ON998E2PHjkVwcDCKioqQmpqK3bt34969e4pb7PWhSZMmGD9+PJKTk1GnTh388MMPePz4MTZt2qRYRx8xvfnmm9i7dy/eeust9OvXD+np6VizZg0CAwOVkmx7e3sEBgZi586daNKkCdzc3NCiRQu0aNEC7777Lr777juEhYVh/PjxyM7Oxpo1a9C8eXOVOrbK/O9//0OXLl3QsmVLTJgwAQ0bNsTjx4+RlJSEf/75B1euXHntPoYPH46VK1di3rx5aNmypeK2Z7k+ffqgbt266Ny5M+rUqYPr169j1apV6NevH5ydnQEA58+fR8+ePTFv3jyDt/fX9Gdp8eLFOHLkCLp3746JEyciICAAmZmZ2LVrF06dOgUXFxeNPwv/+c9/kJOTg5CQEHh5eeH+/ftYuXIlWrdurXi9AgMD0aNHDwQHB8PNzQ0XLlzA7t27MW3atCrPp3Xr1rCyssLXX3+NvLw82NraIiQkBB4eHoiNjcWYMWPQtm1bjBgxAu7u7sjIyMCBAwfQuXNnrFq1Sq+vrUQiQa9evTBs2DDcvHkTq1evRpcuXTBgwAAAFSPm1Y2pWbNmaNSoET7++GM8fPgQQqEQe/bsUVtDJP99+P777yMsLAxWVlYYMWIERCIR3n77baxcuRI8Hg+NGjXCH3/8oVWdTffu3TFp0iTExMQgJSUFffr0gY2NDW7duoVdu3ZhxYoViuaxnDP+DWA116FDh9i7777LmjVrxpycnJhAIGD+/v5s+vTp7PHjxyrr//DDD6xNmzbM1taWubq6su7du7P4+HjF86dPn2YdO3Zk9vb2rH79+opb2fHK7YSFhYVs1KhRzMXFRdFUUO63335TNLzDK7cbXr58mQ0ePJjVqlWL2draMl9fXzZs2DB27NgxxTryW66fPHmi0WtQWVPBV1XWVPDo0aOsc+fOzN7engmFQta/f3+1jdAWLlzIPD09GZ/PV7oNXZNbzy9evMgAsDlz5lS6zr179xgANmPGDKX9vgz/31RQU1lZWaxfv37M2dlZpalgQUEBi46OZv7+/kwgELDatWuzTp06sW+//VZxm+vLTeReVtlrLr9V9uXbil9uKhgUFMRsbW1Zs2bN1L5f1YmJsYpblxcvXsx8fX2Zra0ta9OmDfvjjz9UbhtnjLEzZ86w4OBgJhAIVG75/fHHH1nDhg2ZQCBgrVu3ZnFxcVU2FVTnzp07LDIyktWtW5fZ2NgwT09P9uabb7Ldu3erXV/duXh7ezMA7KuvvlJ5fu3ataxbt26Kn6VGjRqxTz75hOXl5SnWkb9P6m6xf1VlTQVf9rpz1vRn6f79+ywyMpK5u7szW1tb1rBhQzZ16lSltgGafBZ2797N+vTpwzw8PJhAIGA+Pj5s0qRJLDMzU7Gfr776irVv3565uLgwe3t71qxZM7Zo0SKV9hzqrF+/njVs2JBZWVmp/IyfOHGChYWFMZFIxOzs7FijRo3Y2LFj2YULFxTryBvnvap79+5q2wa8+h682lTQ1dWVOTk5sXfeeUeleWd1Y2KMsbS0NNa7d2/m5OTEateuzSZMmMCuXLmi8nuzvLycTZ8+nbm7uzMej6f0e+rJkydsyJAhzMHBgbm6urJJkyYpGryqaypYmXXr1rHg4GBmb2/PnJ2dWcuWLdmnn37KHj16VOk2xsZjTIcKK0KIRfLz80OLFi3wxx9/cB0KIWZF3lgvOTm50hYahDtUs0MIIYQQi0bJDiGEEEIsGiU7hBBCCLFoVLNDCCGEEItGIzuEEEIIsWiU7BBCCCHEolFTQVS0/3/06BGcnZ31MtUAIYQQQgyPMYaCggLUr19fZe6ul1Gyg4r2+JV1yiWEEEKIaXvw4IHa2eHlKNkBFO3aHzx4AKFQyHE0hBBCCNFEfn4+vL29Fd/jlaFkB//OCisUCinZIYQQQszM60pQqECZEEIIIRaN02QnNjYWQUFBihEVsViMQ4cOKZ5ft24devToAaFQCB6PV+V08aWlpWjdujV4PB5SUlIMHzwhhBBCzAKnyY6XlxeWLFmCixcv4sKFCwgJCcHAgQNx7do1AEBxcTHCw8Mxe/bs1+7r008/Rf369Q0dMiGEEELMDKc1O/3791d6vGjRIsTGxuLs2bNo3rw5PvzwQwBAQkJClfs5dOgQjhw5gj179iiNDBFCCCGEmEyBslQqxa5du1BUVASxWKzxdo8fP8aECRPw66+/wsHBwYAREkIIIcQccZ7spKamQiwWo6SkBE5OTti3bx8CAwM12pYxhrFjx2Ly5Ml44403cO/ePY22Ky0tRWlpqeJxfn6+LqETQgghxAxwfjdW06ZNkZKSgnPnzmHKlCmIiopCWlqaRtuuXLkSBQUFiI6O1uqYMTExEIlEin/UUJAQQgixXCY363nv3r3RqFEjrF27VrEsISEBPXv2xPPnz+Hi4qJYPmjQIOzfv1/p/nqpVAorKyu888472LJli9pjqBvZ8fb2Rl5eHvXZIYQQQsxEfn4+RCLRa7+/Ob+M9SqZTKaUiFTl+++/x1dffaV4/OjRI4SFhWHnzp3o0KFDpdvZ2trC1ta22rESQgghxPRxmuxER0cjIiICPj4+KCgowPbt25GQkIC4uDgAQFZWFrKysnD79m0AFfU9zs7O8PHxgZubG3x8fJT25+TkBABo1KhRlXNkEEIIIURzknIZtiXdw/2cYvi6OWCM2A8Ca84rYTTGabKTnZ2NyMhIZGZmQiQSISgoCHFxcQgNDQUArFmzBgsWLFCs361bNwDApk2bMHbsWC5CJoQQQmqUmINpWH8yHbKXil4WHbyOCV0bILqvZjcUcc3kana4oOk1P0IIIaQmiTmYhrV/plf6/KRu3CY8mn5/m88YFCGEEEKMRlIuw/qTlSc6ALD+ZDok5TIjRaQ7kytQJoQQQgj3tiXdU7p0pY6MVaw3vmtDtXU9VnwezqfnILugBB7OdmjfwA1W/KpnKDcESnYIIYQQouJ+TrHG66mr6/nqwHU4CKxQJJEqltUT2WFe/0CEt6in73CrRJexCCGEEKLC102zKZhuZRVg7Z/pKqNADFBKdAAgK68EU368hMNXM/UUpWYo2SGEEEKIilEdfDVa79y9HI33Kc+HFuxPg/R118j0iC5jEUIIIUShsKQcM3Zexo2sAo3W1zZnYQAy80pwPj0H4ka1tA9QB5TsEEIIIQQAMGDVSfz1j3Emx84uKDHKcQC6jEUIIYQQGDfRAQAPZzujHYtGdgghhJAarrCk3GiJDg9AXVHFbejGQskOIYQQUkNJZQzn03Ow6ECaUY4n77Azr3+gUfvtULJDCCGE1ECHr2Ziwf40ZOYZr3amLkd9dijZIYQQQmqYw1czMeXHSzDWzd9+tRwQMziIOigTQgghxPCkMoYF+9OMlugAwG9Tu0DkYGPEIyqju7EIIYSQGuR8eo5RL1351rLnNNEBaGSHEEIIqRHkE3Um/P3EaMf0rWWPxE9CjHa8ylCyQwghhFg4dRN1GgoPQFsfF/wwtj3nIzpylOwQQgghFizmYBrW/plutOMlfdYLdV2M1zBQE1SzQwghhFgoSbkM608aL9EBADcngVGPpwlKdgghhBALI5UxJN15hk93XzHKpauXbUu6Z9wDaoAuYxFCCCEWhItmgS+7n1PMyXGrQskOIYQQYiGM3SxQHV83Bw6Prh5dxiKEEEIsABfNAtUZ3s6H4whUUbJDCCGEWABjNwuszM7kDK5DUEHJDiGEEGIBsgu4T3QA06zZoWSHEEIIsQAezqbR24ZqdgghhBBiED6u3CcZfB4wRuzHdRgq6G4sQgghxARJZQzn03OQXVACD2c7tG/gBqmMYVvSPdzPKYavmwPGiP2QV1yGt1afwj+53F/GmtC1AQTWpjeOQskOIYQQYmLU9cpxEFjhRZkU7KXbrRYeuM5BdKr4vIpEJ7pvINehqEXJDiGEEGJCKuuVUyyRchLP6wT7uODniWKTHNGRM93ICCGEkBrGVHrlaGPLux1MOtEBKNkhhBBCTIap9MrRRurDPK5DeC1Ok53Y2FgEBQVBKBRCKBRCLBbj0KFDiufXrVuHHj16QCgUgsfjITc3V2UfAwYMgI+PD+zs7FCvXj2MGTMGjx49MuJZEEIIIfphKr1ytGEOMXOa7Hh5eWHJkiW4ePEiLly4gJCQEAwcOBDXrl0DABQXFyM8PByzZ8+udB89e/bEL7/8gps3b2LPnj24c+cOhg4daqxTIIQQQvTGVHrlaMMcYuYxxkzq0qCbmxuWLl2K8ePHK5YlJCSgZ8+eeP78OVxcXKrc/vfff8egQYNQWloKGxsbjY6Zn58PkUiEvLw8CIXC6oRPCCGE6CzjaTG6fXuC6zA0xucBNxZGcFazo+n3t8ncjSWVSrFr1y4UFRVBLBbrtI+cnBz89NNP6NSpU5WJTmlpKUpLSxWP8/PzdToeIYQQ8jJ1vXGs+LzXbvdCIsXig2n46azpzStVFRkDLt5/DnGjWlyHUiXOk53U1FSIxWKUlJTAyckJ+/btQ2Cgdvfpz5o1C6tWrUJxcTE6duyIP/74o8r1Y2JisGDBguqETQghhChR1xunnsgO8/oHIrxFvUq3m7A1GfFp2cYI0SCoZkcDTZs2RUpKCs6dO4cpU6YgKioKaWlpWu3jk08+weXLl3HkyBFYWVkhMjISVV2di46ORl5enuLfgwcPqnsahBBCajB5b5xX76TKyivBlB8v4fDVTLXbmXuiA5hHzQ7nIzsCgQD+/v4AgODgYCQnJ2PFihVYu3atxvuoXbs2ateujSZNmiAgIADe3t44e/ZspZfDbG1tYWtrq5f4CSGE1GxV9cZhAHgAFuxPQ2hgXaVLWi8kUrNOdHgA6ooqLtWZOs5Hdl4lk8mU6ml02R5AtfZBCCGEvI5UxpB05xmWxd+ssjcOA5CZV4Lz6TlKyxcf1O4qhimRp2zz+gdqVJPENU5HdqKjoxEREQEfHx8UFBRg+/btSEhIQFxcHAAgKysLWVlZuH37NoCK+h5nZ2f4+PjAzc0N586dQ3JyMrp06QJXV1fcuXMHc+bMQaNGjXQuciaEEEJeR119zuu8Wtty71mxvsMyCB4PsLexUpquoq4GtUimhNNkJzs7G5GRkcjMzIRIJEJQUBDi4uIQGhoKAFizZo1SIXG3bt0AAJs2bcLYsWPh4OCAvXv3Yt68eSgqKkK9evUQHh6OL774gi5TEUIIMYjK5q56nVdrW/xqOeDkLf3FpW9N6zhh2BveGCP2gxWfp9NdZqbC5PrscIH67BBCCNGEVMbQ5evjOk3pcHV+GJzs/h1jSM8uQs/vEvQYnX69Gq8p0vT72+RqdgghhBBTVZ25q74+fF3p8dtrT+sjJIMxhzmvNGXaKRshhNRwujapI4ZRnZ4y8hodeQPBp0Vl+grLIMyhf46mKNkhhBATpWuTOmI41ekp41fLwaz66phD/xxN0WUsQggxQbo2qSOG1b6BG3QdWHv4vMhsEh1XBxuz6J+jKUp2CCHExLyuSR1Q0aROKqvx95cYXWFJOXR52QXWfBy/+Uz/ARmIpX2y6DIWIYSYCHktR8qDXI2b1Jn6BIzmSF2dlFTGsC3pHmITb+u0T0m5TM9RGlZucZlFfb4o2SGEEBOgSy2HJRWQmgp1dVIOAiu8KJOipjVqsaTPFyU7hBDCMV2LVi2pgNQUVNYs8OXOwTWJJX2+KNkhhBAO6ToZJJ8HBPu6GiCimqmqOqmaxpwm+NQUJTuEEMIhXSeDlDHg4v3nFlNTwRVJuQzbku7hXPoznZsFWhJzm+BTU5TsEEIIh6ozGaQl1VRwIeZgGtafTNfp7ipLZW4TfGqKkh1CCOFQdSaDtKSaCmOLOZiGtX+mcx2GyRjUuj6Gt/Ox2A7d1GeHEEI4NKN3U6234aGik7Il1VQYk6RchvUnKdGR4/OAb4a2grhRLYtMdAAa2SGEEE4UlpRjxs7LOJ+eo9P2r9ZUyGtP7ucUw9fNAQNaeWLyjxfwKK8E9UV2+GFse4gcbPQVvlmSv+Yp/+TSpauXTOjaAAJryx774DFW0zoHqNJ0inhCCNGHAatO4q9/8nXePjTQA+sj2ykea1p74lvLHomfhOh8XHNW3dfcEvF5FYlOdN9ArkPRmabf35adyhFCiInRx5dufFo2Yv7/Li557YkmIxX3n71A96XHq3Vsc2ROiY6DjeG/lnk8YE6/ANxYGGHWiY42KNkhhBAjKSwp19uX7vqT6SgsKde69uT+sxfIKy7TSwzmQJ+vuTFsG9fR4MeI/6A7xndtaPGXrl5Wc86UEEI4IpUxJN15hhHrkvS2TxkDOiw+qlPtybubz+stDlM3Y+dlrkPQypB1Zwx+jCdFpQY/hqmhAmVCCDEgdXMt6UuRjtMYPKpBzfMynr/gOgSTUxP7M9HIDiGEGIh8riVT68xbX1Rz+vP4uNpzHYLJqYn9mSjZIYQQAzDluZbWjH6D6xCMZlZYANchmIya3J+Jkh1CCDGA8+k5JjeiI/f7lYdch2BwLyRSzPk1FRHfJ3Idikmw1DmvNEU1O4QQYgCmXBdxP0f3+bjMwYStyTrNJG/JLHXOK01RskMIIQZgynURvm4OXIdgMKac6LT1FuHSgzyjHtPNwQb/eyfYYue80hQlO4QQYgCtvV24DkEtPg8YI/bjOgyDeCGRmmSiw0PFyMplIyc6APDre13gU9tyk1tNUc0OIYQYwPZz97kOQS1LnAdJUi7DxpN3MfB/J7kORYV8LKVzo1qcFKuvP3WHg6OaHhrZIYQQAzC1uhhLmAdJHU3nBeOKvFbmzJ1nnBz/3jPT+hxyhZIdQggxAFOqiwkN8MD/3gm2uBEd+bxgpmhQ6/oY3s5HUSvzkKPmhn61TOdzyCXL+uQTQoiJGN7Oh+sQAFSM6FhioiMpl2k9L5ix8HnAN0NbQdyolqIomKvPw8d9mnFyXFNjWZ9+QggxETuTM7gOAYBl1ugAwLakeyZ76Urda87V52H3xQecHNfUcPoTEBsbi6CgIAiFQgiFQojFYhw6dEjx/Lp169CjRw8IhULweDzk5uYqbX/v3j2MHz8eDRo0gL29PRo1aoR58+ZBIpEY+UwIITWRvHHdmI3nMOfXVLyQSHHzUQEaRR/AwgPXOY2NzwMmdbO8Gh35a77hlOmN6lT1mnNVw2VqtWNc4bRmx8vLC0uWLEHjxo3BGMOWLVswcOBAXL58Gc2bN0dxcTHCw8MRHh6O6Ohole1v3LgBmUyGtWvXwt/fH1evXsWECRNQVFSEb7/9loMzIoTUFK/2czl5C9h2lvvRHHdHG0zu4Y8xYj+LG9Ex1R46fQLroEMDtypfc65quEypdoxLPMaYSQ0Eurm5YenSpRg/frxiWUJCAnr27Innz5/DxcWlyu2XLl2K2NhY3L17V+Nj5ufnQyQSIS8vD0KhUNfQCSE1hKl+6QLAlbl9IHKw4ToMvTPF11zeP+fUrJDXNuyTlMvQbM4ho196+/urCItLel+m6fe3ybwCUqkUO3bsQFFREcRisc77ycvLg5tbzZvkjBBiHKbauE7OErvkmuJrru1cUwJrPiZ0bWDYoNSQmmphk5FxnuykpqbCyckJtra2mDx5Mvbt24fAQN2uMd++fRsrV67EpEmTqlyvtLQU+fn5Sv8IIaQqecVlGLL6NNoujOc6lCrN2HmZ6xD0SlIuw+gNZ7kOA1avfFvWFdkhdnRbreaaiu4biEndGsCY+ejig2nGO5gJ47zPTtOmTZGSkoK8vDzs3r0bUVFRSExM1DrhefjwIcLDw/H2229jwoQJVa4bExODBQsWVCdsQkgN0n3pcdx/xk2fFG1lcNTPxRBMoWFgsI8LfhjbHk521jifnoPsghJ4ONvpPNdUdN9AfNSnGbYl3cMvF//BzawCA0T9L2oqWIHzZEcgEMDf3x8AEBwcjOTkZKxYsQJr167VeB+PHj1Cz5490alTJ6xbt+6160dHR2PmzJmKx/n5+fD29tY+eEKIxTOnRAcAfFztuQ5BL0yhYWATD0fsea+z4rG4US297Fdgzcf4rg0BwOB37VFTwQqcX8Z6lUwmQ2lpqcbrP3z4ED169EBwcDA2bdoEPv/1p2Rra6u43V3+jxBCXpVXXGZWiQ4AfD2kFdchVJupNAz86T+6149qontjD4PuH6CmgnKcjuxER0cjIiICPj4+KCgowPbt25GQkIC4uDgAQFZWFrKysnD79m0AFfU9zs7O8PHxgZubmyLR8fX1xbfffosnT54o9l23bl1OzokQYv5yCiUYse4Mbj8p4joUre27/I9i1MBcmUrDwNtPCuEutNX7fjOeFiN8RSKKy2R63/erdl98YPafB33gNNnJzs5GZGQkMjMzIRKJEBQUhLi4OISGhgIA1qxZo1Rb061bNwDApk2bMHbsWMTHx+P27du4ffs2vLy8lPZtYnfUE0LMRLuv4vGk0Hwbk1pCEzlTOYfsghK979N/9gGUGz7HUTCV15JrnCY7GzdurPL5+fPnY/78+ZU+P3bsWIwdO1a/QRFCaixzT3QAy2giZyrn4OFsp9f9GTvRAUznteSaydXsEEIIF3IKJWaf6PB5wBixH9dhVNvQYNO4YaS1t4ve9pXxtNjoiY6lfB70gZIdQkiNJymXoc+yBK7DqLYOfm746kAaNp68i8KScmw8eRdzf7uKjSfvQmLsb9pq+PbIDa5DAABsP3dfb/sKX5Got31pylIngdUF57eeE0IIl0yhl4u+JKXnICk9B4DqLc2LDl7HhK7mMTGoqfSG0We9ywsjFCPL8Xkwm/faWCjZIYTUWKbQy8VYZAyKczX1L0G/Wg44eYvrKPRb72Jvwzf43Vc2Vjx8Ft7MIieBrS56NQghNZKp9HIxtvUn003+ktbIN3y5DgGAfmuHfp/aVW/7qsyh6d0wvmtDSnTU0Ghk5+Vuw5r64osvaEJOQojJMpVeLsYmYxXnboq9V6QyhvPpORi9kfu5sICK2qGFg1rqZV+Jtww/kemWs+l6i9fSaJTsLF++HGKxGAKBQKOdnjp1CtOmTaNkhxBismpy/xFTPPfDVzOxYH8aMvP039tGV/qsHTLGa24qtU6mSOOanX379sHDQ7PW1s7OzjoHRAghxlCT+4+Y2rkfvpqJKT9egqkNtOlzXiljvOY0D1blNLqwt2nTJohEIo13unbtWtSpU0fnoAghxNDeauP1+pUs1PB2PlyHoCCVMSzYn2ZyiQ6g33mljPF5m23ihedc0mhkJyoqCuXl5a9dLy0tDYGBgRg1alS1AyOEEEN4mPMCEd8nIr9EynUonPni11QMb+eD9g3cYMXn6XXfknIZtiXdw/2cYvi6OWCM2A9WfB7Op+cgu6AEHs52aN+gosThfHoOTt9+alKXrl6mr3mlCkvKDd7HKTTQA/YCK4Mew5xpfBnrnXfewc6dOyt9Pi0tDSEhIcjKytJLYIQQom9NPj8IidQUxxCM69eUR/g15RHqiewwr38gwlvU08t+1fUs+urAdTgIrFAk+Te5dHGwAQDkFpfp5biGoo86mwGrTuKvf/L1EE3lQgM9sD6ynUGPYe40vj8tKSkJkydPVvvc9evXERISgk6dOuktMEII0SdKdFRl5ZVgyo+XcPhqZrX3Je9Z9OodbgxQSnSAiiTH1BMdoPp1NoZMdKx5wJiOPrj+ZTglOhrQeGQnLi4O3bp1g5ubGxYvXqxYfuPGDYSEhKBjx47YtWuXQYIkhJDqeJjzghIdNRgAHoAF+9MQGlhX50taltqzqDq1TYUl5QYd0Tn+UU/41KaCZE1pPLITEBCAgwcPYtWqVfj2228BVCQ6PXv2RLt27bB7925YWdH1QkKI6cgplKDPdwno/M1xrkMxWQxAZl4Jzv//NBO6sNSeRTuTM7TeRlIuw8aTdw0+F1b8dSoZ0YZW00W0a9cOv/76K958800UFhZi/fr1CA4Oxu7du2FtTTNPEEJMR7uv4s1+FnNjyi7QvUjYFPv26IO252XMedYs9TU3FK0zlJCQEGzfvh1vv/02+vTpg3379sHGxsYQsRFCiE4o0dGeh7OdztuaWt8efdHmvIw9z5qlvuaGonGy4+rqCh5P+XruyZMnVfrp5OToPhRKCCHVlVMooURHS3weEOzrqvP2Y8R+WHTwukVdyuLzKs5LE8auWdImNlJB42Rn+fLlBgyDEEL0Y8S6M1yHYHZkDLh4/znEjWrptL3Amo8JXRtY1AzyE7o20HhCTWPXLGkTG6nAY4xZUC6um/z8fIhEIuTl5UEoFHIdDiFEB4Ul5Zix8zKOXs82yW68pq5pXWe093PF7L6BapvTySfpfLkx4AuJFDN2XkbG8xfwcbWHl6sDNp25Z/zg9YjPq0gmojXoRpxXXIZ3N5/H9cwCFJcZvkmlNrHVFJp+f1OyA0p2CDF3xmjcVpO82qRO3SSd1nweyi3oulUTDycMb+eNMWI/jUZNui89jvvPXhghMuANX1dEtKircWw1iabf3xq9am5ubnj69KnGB/fx8cH9+/c1Xp8QQnRFiY7+xadlY8LWZAD/TtL56pQOlpToAMBv07pgfNeGJpfo8HnA9gkdNY6NqKdRzU5ubi4OHTqk8WSgz549g1Rac+edIYQYh6Ebt9Vk8WnZKCwpN9lJOvVJm3ml8orLjJboAFSfoy8aFyhHRUUZMg5CCNGYvD7nzJ1nXIdi0UasSzLZSTr1pXeAO97t3BC/pTxU1CKp6yQtr8/5659co8RF9Tn6pVGyI5PJDB0HIYRohC5bGc/VR5b5OsuLsd/wc8OSQzcwcv1ZxXPqJkc15mUrKx4wu28A1efoGb2ShBCzQYkO0Yf5/Zujs39tfLgjRWXk6tXJUY2Z6AAVyRbV5+gfvZqEELNA9TlEX1p6iiqtRZIvW7A/DTmFEqMmOgCwc2Inox6vpqBkhxBi0l5IpJjzayq60WSeRE8mb7tQZS2SfHLUDovjjRfU/zt8LdPox6wJaPZOQojJmrA1GfFp2VyHQSzMKQ0L28s4KFelCT4Ng0Z2CCEmiRIdUhPRBJ+GoVOyc+fOHXzxxRcYOXIksrMrfhkdOnQI165d02twhJCa6YVESokOqXFogk/D0TrZSUxMRMuWLXHu3Dns3bsXhYWFAIArV65g3rx5eg+QEFJzSGUMSXeeKbr3ElKTUANBw9H6Vf3ss8/w1VdfIT4+HgKBQLE8JCQEZ8+erWJLVbGxsQgKCoJQKIRQKIRYLMahQ4cUz69btw49evSAUCgEj8dDbm6uyj4WLVqETp06wcHBAS4uLtqeDiHERBy+mokuXx/HyPVnceo2NQskNQefB0zqRg0EDUnrAuXU1FRs375dZbmHh4dW82cBgJeXF5YsWYLGjRuDMYYtW7Zg4MCBuHz5Mpo3b47i4mKEh4cjPDwc0dHRavchkUjw9ttvQywWY+PGjdqeDiHEBMjnX7L0aQlIzSaw4sGvlgO2vtsRB1If4X5OMXzdHKiBoBFoney4uLggMzMTDRo0UFp++fJleHp6arWv/v37Kz1etGgRYmNjcfbsWTRv3hwffvghACAhIaHSfSxYsAAAsHnzZq2OTQgxDVIZqxHzLxFyaU4fONlVfO2O79qQ42hqFq2TnREjRmDWrFnYtWsXeDweZDIZTp8+jY8//hiRkZE6ByKVSrFr1y4UFRVBLBbrvB9NlJaWorS0VPE4P58alRFiSJJyGbYl3VP6S1YqY1h8MA0pD3Itfv4lQgAg9WEexI1qcR1GjaR1srN48WJMnToV3t7ekEqlCAwMhFQqxahRo/DFF19oHUBqairEYjFKSkrg5OSEffv2ITDQsNctY2JiFCNChBDDijmYhvUn0yF7aehm4YHr3AVECEeyCyip54pWFwkZY8jKysL333+Pu3fv4o8//sCPP/6IGzduYNu2bbCystI6gKZNmyIlJQXnzp3DlClTEBUVhbS0NK33o43o6Gjk5eUp/j148MCgxyOkpoo5mIa1fyonOoTUVB7OdlyHUGNpNbLDGIO/vz+uXbuGxo0bw9vbu9oBCAQC+Pv7AwCCg4ORnJyMFStWYO3atdXed2VsbW1ha2trsP0TQiouXa0/mc51GBrhAajtaI0nReVch0IsEA9AXZEd2jdw4zqUGkurkR0+n4/GjRvj2TPD3RYqk8mU6mkIIaZN3hvnt5SHSLrzDC8kUmw8eRej1ieZxYgO7///u/CtIAR5CTmNhVge+edrXv9AWPF5Va5LDEfrmp0lS5bgk08+QWxsLFq0aFGtg0dHRyMiIgI+Pj4oKCjA9u3bkZCQgLi4OABAVlYWsrKycPv2bQAV9T3Ozs7w8fGBm1tFhpyRkYGcnBxkZGRAKpUiJSUFAODv7w8nJ6dqxUcIqdrhq5lYsD/NrAuM64rsMK9/IMJb1EN4i3oYsOqk2tnVrfk8lJtD9kY4wwPgILBCkUSqWPby54twh8cY0+qn19XVFcXFxSgvL4dAIIC9vb3S8zk5ORrva/z48Th27BgyMzMhEokQFBSEWbNmITQ0FAAwf/58tYXEmzZtwtixYwEAY8eOxZYtW1TWOXHiBHr06KFRHPn5+RCJRMjLy4NQSH/ZEaIJc++NM62nPzr710b7Bm4qf3EXlpRjxs7LyHj+Aj6u9lg2vA3sBVY4n56D7IISLD6Yhsf5Eo4iJ6ZkaFtPONhaK+4ytOLzFJ8TD2c7tZ8voj+afn9rneyoSyxeFhUVpc3uTAIlO4RoRypj6PL1cbMc0ZHXT5yaFaLTl1BecRlafXlE/4ERs1PdzxKpPk2/v7W+jGWOyQwhRL/Op+eYbaIDVK9+4t3N5/UXEDFrDEBmXgnOp+dQ/xwTp3Wyk5GRUeXzPj4+OgdDCDEdUhmrdDjeXPuFVKd+Qv563HlSZIDIiDkz15+HmkTrZMfPzw88XuV/EUml0kqfI4SYB3WFx/VeShTMrV9Ig1oOWDw4SOf6CUsoxCaGY24/DzWR1snO5cuXlR6XlZXh8uXL+O6777Bo0SK9BUYI4UZlhcdZeSWY8uMlxI5ui9DAunBxsEFucRknMWrr16ldIHKw0Wlbcy/EJoZD/XPMh9bJTqtWrVSWvfHGG6hfvz6WLl2KwYMH6yUwQojxVTUpJ0PFL/cF+9MQ0qyOkSPTnW8te50THZqklFSG+ueYF62Tnco0bdoUycnJ+todIcSI5Lda38gqqPJSjbwgc9T6JLMY1fGtZY/ET0Jeu96rE5UOb+eDnckZOGemhdhEleMr/W+0JbDmQ1IuUzym/jnmRetk59UZwhljyMzMxPz589G4cWO9BUYIMY7KmuhV5cL9XMMEo2ci+9eP6NBEpZata+PaeK+HP54XSfDlH2nIytcteRXaWmHluPbUP8dMaZ3suLi4qBQoM8bg7e2NHTt26C0wQojh6ZLomJO//snHgFUn8fu0rmqfl09USixXjybuyHshwdTt1au7elpUhvd/voTkL0L1FhsxHq2TnRMnTig95vP5cHd3h7+/P6yt9XZVjBBiYIUl5Rad6Mj99U8+CkvK4WSn/PvJnCYqJbob3s4HocsS9VJ39aRQgpxCCdycBHrYGzEmrbMTHo+HTp06qSQ25eXl+PPPP9GtWze9BUcIMZwZOy+/fiUL0WFRPDr518ay4W0glTG8u/k8/s4uMIuJSkn1tFl4BGVS/b3RI9adwZGZPfS2P2IcWic7PXv2RGZmJjw8PJSW5+XloWfPntRnhxAzkfH8BdchGE1RmQzx17PRYn4c16EQI9NnogMA2QU0J5o54mu7AWNMbVPBZ8+ewdHRUS9BEUIMz8fV/vUrcYTqPomp8nCmS1jmSOORHXn/HB6Ph7Fjx8LW1lbxnFQqxV9//YVOnTrpP0JCiEEsHNgS8dePcR2GWueie6NDzFG6zERMzo6J9D1njjROdkQiEYCKkR1nZ2fY2//7V6FAIEDHjh0xYcIE/UdICDGIOb+lch1Cpb4//jcmdG1Ad0oRk+LuJKDiZDOlcbKzadMmABVzY3388cd0yYoQM2fKNTv3nhVj2/gOAKDSA4cQLrg7Cei2czOmdYHyvHnzDBEHIcTIfFztcTOrgOsw1PKr5QAAiO4biI/6NMO2pHv45cID3HxcyHFkpKZp4uGIHRM70YiOmeMxxrT+m2n37t345ZdfkJGRAYlEuTL90qVLegvOWPLz8yESiZCXlwehUMh1OIQYRWFJucnenXRlbh+V+azyisvQ6ssjHEVEaqL973VBSx8R12GQKmj6/a313Vjff/89xo0bhzp16uDy5cto3749atWqhbt37yIiIqJaQRNCDO+FRIo5v6Ziyk8XUUvHCTINbffFBxotI8SQfrmUwXUIRE+0voy1evVqrFu3DiNHjsTmzZvx6aefomHDhpg7dy5ycnIMESMhRE8mbE1GfFo212G81v2cYo2WEWJI957RZ85SaD2yk5GRobjF3N7eHgUFFdf8x4wZg59//lm/0RFC9MZcEh0A8HVz0GgZIYYkrx0j5k/rZKdu3bqKERwfHx+cPXsWAJCeng4dyn8IIUbwQiI1m0SHzwPGiP1UlvdrWd/4wRBO7JnYySQaS87uG8h1CERPtL6MFRISgt9//x1t2rTBuHHjMGPGDOzevRsXLlxQNB4khBiWVMZwPj0H2QUl8HC2Q/sGbgCgskxSLsPig2mIT3vMccSam9C1AQTW//4dlvG0GOErElFcJuMwKmJMEibjvM9SaKAH7AVWnB2f6JfWyc66desgk1X80pk6dSpq1aqFM2fOYMCAAZg0aZLeAySEKDt8NRML9qchM69Esczl/wuNc4vLFMtsrfkoLTefBIHPq0h0ol/6a9p/9gGY0SkQPckuKFF8DrjosxQa6IH1ke2Me1BiUFrdel5eXo7Fixfj3XffhZeXlyHjMiq69ZyYi8NXMzHlx0uwpAvGfQLroEMDN4wR+ymN6FCiU3P9PKEjxI1qAQAk5TJsS7qH1Sfu4Fmx4SbhdHOwQb+gepjdN5BGdMyIQW49t7a2xjfffIPy8vJqB0gI0Y5UxrBgf5pFJTp8HrBqVFuM79pQ5dIVJTo1Dw9APdG/l2UBQGDNx/B2PgZNdABgz+TOWDioJSU6FkrrAuVevXohMTHRELEQQtTIKy7DkNWn0W5RvNKlK0sgY8DF+88Vj+U9gLp/e4LDqAhXGAAwhuCF8ejzXQJyCiUoLClHj6XHDX7sMT+cNfgxCHe0rtmJiIjAZ599htTUVAQHB6vMkTVgwAC9BUdITdd96XHcf2a6c1jpQ3ZBRQJnTrfGE8PJzC8FAOS+KEPbr+KNdtycorLXr0TMltbJznvvvQcA+O6771Se4/F4kEql1Y+KEFIjEh0A8HC2o0SnhnC04aO2sy3u55je59rN0TS7iRP90Poylkwmq/QfJTqE6EdecZnFJzry+oyWniJKdGqIYx/1xIPnpvm53vdeF65DIAakdbLzspISy6ofIMRUvLv5PNchGJS8X9y8/oH4+vB1TmMhxjPnt1Sj30auCaGdNdyFtlyHQQxI62RHKpVi4cKF8PT0hJOTE+7evQsAmDNnDjZu3KjVvmJjYxEUFAShUAihUAixWIxDhw4pnl+3bh169OgBoVAIHo+H3NxclX3k5OTgnXfegVAohIuLC8aPH4/CwkJtT4sQvZLKGJLuPMNvKQ+RdOcZpJX8hpcX5I7ZeA5zfk1FYUk5ku48w50nRUaO2Ljqiuzwv1FtIbIX4Py956/fgFiEDBMc1RHaWeOv+WFch0EMTOuanUWLFmHLli345ptvMGHCBMXyFi1aYPny5Rg/frzG+/Ly8sKSJUvQuHFjMMawZcsWDBw4EJcvX0bz5s1RXFyM8PBwhIeHIzo6Wu0+3nnnHWRmZiI+Ph5lZWUYN24cJk6ciO3bt2t7aoTohbqmf/VEdpjXPxDhLeoplr1ap3LyFrDtbM2YZblYUo7Zv6YqNUHUBQ+wqFvxLZ2Pqz1uZhUY/bhCOyscer87BNZ8vLX6FHKKyuDmaIN973WhEZ0aQqumggDg7++PtWvXolevXnB2dsaVK1fQsGFD3LhxA2KxGM+fV++vNDc3NyxdulQpaUpISEDPnj3x/PlzuLi4KJZfv34dgYGBSE5OxhtvvAEAOHz4MPr27Yt//vkH9etrNpcONRUk+lJZ0z/5ZZvY0W0R3qIeFeTqyelPQ9Dt2+OQUk8es3B1fhhazI8z6jEPTuuKQC/6vW6pDNJUEAAePnwIf39/leUymQxlZbr/lSaVSrFjxw4UFRVBLBZrtE1SUhJcXFwUiQ4A9O7dG3w+H+fOndM5FkJ0UVXTP/myBfvTUFhSTomOHtjb8OFkZ02JjhkpLDF+Q9qm9Z2NfkxierS+jBUYGIiTJ0/C19dXafnu3bvRpk0brQNITU2FWCxGSUkJnJycsG/fPgQGajbTbFZWFjw8PJSWWVtbw83NDVlZWZVuV1paitLSUsXj/Px8reMmRE4+Kefp20+qbPrHAGTmlaDrN4ZvkGbp7G34uL4wAkNWn+Y6FKKF3ssSjH7M8+k5iqknSM2ldbIzd+5cREVF4eHDh5DJZNi7dy9u3ryJrVu34o8//tA6gKZNmyIlJQV5eXnYvXs3oqKikJiYqHHCo4uYmBgsWLDAYPsnNYe6+pzXeV7NOpWarLajDf6Y3g11XewAAI8srKO0pSssNX57EnnTSlKzaX0Za+DAgdi/fz+OHj0KR0dHzJ07F9evX8f+/fsRGhqqdQACgQD+/v4IDg5GTEwMWrVqhRUrVmi0bd26dZGdrXw5oLy8HDk5Oahbt26l20VHRyMvL0/x78GDB1rHTYi8PsfSpnAwVV4udrgwp48i0QGA+iK7KrYgpKJpJSFaj+wAQNeuXREfb5g23jKZTOkSU1XEYjFyc3Nx8eJFBAcHAwCOHz8OmUyGDh06VLqdra0tbG2pAp/ozhIn5TR1uyd3Vlm2PrKdUacUIKaDB6CO0A6P80vU/hzyUNHi4OVJRUnNpVOyAwAXLlzA9esVzcACAwMVyYY2oqOjERERAR8fHxQUFGD79u1ISEhAXFxFtX5WVhaysrJw+/ZtABX1Pc7OzvDx8YGbmxsCAgIQHh6OCRMmYM2aNSgrK8O0adMwYsQIje/EIkQX59NzaETHyNKfFSmN6gDAzcfGv42ZmIaJ3RqgjY8rpvx4SaUFwctNK634PDVbk5pG62Tnn3/+wciRI3H69GnFbeC5ubno1KkTduzYAS8vL433lZ2djcjISGRmZkIkEiEoKAhxcXGKy2Fr1qxRqq3p1q0bAGDTpk0YO3YsAOCnn37CtGnT0KtXL/D5fAwZMgTff/+9tqdFiFaoDsD41L3m9D5YvlcTGT4PmNC1AaL7VtR1xo5uq1I3V1dNXytSs2ndZyc8PBy5ubnYsmULmjZtCgC4efMmxo0bB6FQiMOHDxskUEOiPjtEW0l3nmHk+rNch2F23mpdH/tSHum07c8TOqrcVUPvg2US2lvjg5DGGCP2AwBsS7qH+znF8HVzwBixHwTWyuWm8jsiswtK4OFccemKRnRqBk2/v7Ue2UlMTMSZM2cUiQ5QcUfVypUr0bVrV92iJcTMtPQUGfwYfB5Mch4hXcjrJ74e2gpn03OQlae+zqKqbdXVXhjjfSDGt3dSZ/jXdVI8Ht+1YZXrW/F5dHs5qZLWd2N5e3urbR4olUqpToZYFHXzW+UUStDnuwS0WXjEoMeu7WSDfi0rv6PQnLxcPyGw5mNe/0Cl5Zpuq+4vdZpE1DK9t/0C1yEQC6P1yM7SpUsxffp0/O9//1N0Lr5w4QI++OADfPvtt3oPkBAuqOufY8x5mJ4WlmH/X1mwteajtNy8WwS/Wj8R3qKe2joLVwcbMEBpvqzX1V7ce1Zs0NgtnanOLZZdIOE6BGJhtK7ZcXV1RXFxMcrLy2FtXZEryf/f0dFRad2cnBz9RWpAVLNDXlbZ/Fb64uZgg35B9TC7byAk5TK8u/k87jwtqvakmKZoTr8AjO3cQO2ojLo6CwBa1V7M+TW1xkyeqk99AuugQwM3pZqYc+k5OJL2mNvA/l8TD0ccmdmD6zCIGdD0+1vrZGfLli0arxsVFaXNrjlDyQ6Rk8oYunx93KC3lV+Z2wciBxujHtPY5HU2p2aFGLRQNO2ffPRdddJg+7c0Vb0v8s+hNvVUhnL60xB4utlzHAUxBwYrUDaXBIYQbfw7v9VTgycdozeeRWtvF8zuW1HDsvl0usUlOoBhe5zcfFSAviv/hJTrb2Uz8rr3xYrPw7z+gWr71hjbmj9vY+GglhxGQCyNzk0Fs7OzkZ2dDZlMuZ4gKCio2kERYky6zG9VHakP85H6MB/bzmZYRE3Oqwzd48TvswMG2a+l0+R9qayeykFghRdlUmh3HUB3VItF9E3rZOfixYuIiorC9evX8eoVMB6PB6nU+BO9EaIrQ9fnvI4lJTpvta6PYe18DNrjhBId3VRVO/Wq8Bb1EBpYV6V2SipjKv1upDKGxQfTcOr2U6Q/1V+C4lfLQW/7IgTQIdl599130aRJE2zcuBF16tQBj0eNm4h5ovmt9IfPA74e2kql2Zs+3XxEU0PoalQHX60SUHV9a6z4PLX9bhYOaom84jK0+lJ/7Rhm/393ZEL0Retk5+7du9izZw/8/f0NEQ8hRkPzW+lPBz83fHUgrdIOt9XxQiLF4oNpdNdVNWw/d/+1jfl0Ia91+9/xW3rbZ2igB+wFVnrbHyGADslOr169cOXKFUp2iNmjeZX0Jyk9B0npFa0mFh28rjR3UXVM2JqM+LTsau+nprufo/8aGEPUuoUGemB9ZDu97Y8QOa2TnQ0bNiAqKgpXr15FixYtYGNjo/T8gAED9BYcIYbk4Wz3+pW0YMMHyiynBEdnMgas/TMdAKqV8FCioz++bvqtgdFnrZuTrRXeauOJ2X0DaUSHGIzWyU5SUhJOnz6NQ4cOqTxHBcrEnOh7XqUzn/VGh5ijFjOfVXWtP5mOj/o00+mS1guJlBIdPeHzoGgeqA/6rnU7PauXUt8pQgxB699C06dPx+jRo5GZmQmZTKb0jxIdYg7+nd8qTq/7HbbuDCU6L5Gxis68lZGUy7Dx5F3M/e0qNp68C0m5DHnFZRiy+jTaGnjusZpkQtcGeq2h0metm28te0p0iFFoPbLz7NkzzJgxA3Xq1DFEPIQYVLuv4vGk0DDz7ujz1ltLUVmtSMzBNKw/ma6UHC48QJN66hOfB73VTr1MX7VuvrXskfhJiF72RcjraJ3sDB48GCdOnECjRo0MEQ8hBmPIRIeop65WJOZgmqKmh+jfy/NeGaIVQHVq3ZztrNDEwxk/jG1PIzrEqLROdpo0aYLo6GicOnUKLVu2VClQfv/99/UWHCH6klMooUSHA8Pb+Sg9lpTLsP4kJTqGIJ/3KnZ0sEHnI2vfwA31RHZaz6HF5wEXv+hj0F5MhFRGp7uxnJyckJiYiMTERKXneDweJTvEJI1Yd4brEGqknckZSv1dtiXdo7omAzDGfGRyus6hpe/aIUK0oXWyk55Of5UR85NdQKM6XJDX7BSWlGPGzstIvvec44gsk6HnI3tVZXNoOdpaobhUqpQAGap2iBBt6DwRqEQiQXp6Oho1agRra513Q4hReDgLkPuijOswahxfNwcMWHUSf/2Tz3UoFmlaT3909q9t0PnIKqPNHFo0okO4xmOvzub5GsXFxZg+fTq2bNkCAPj777/RsGFDTJ8+HZ6envjss88MEqgh5efnQyQSIS8vD0KhkOtwiAHkFErQ9qt4rsOoUfg8oHl9IVIfUqKjb/L6nFOzQoye5BBiSjT9/tY63Y6OjsaVK1eQkJAAO7t/q/J79+6NnTt36hYtIQYmsaDZxc3FGLE3JToGYMz6HEIshdbXn3799Vfs3LkTHTt2VJrxvHnz5rhz545egyNEX95c+Senx7ex4qFMWjMqc+U1GnefFHEdikV49bNj7PocQiyB1snOkydP4OHhobK8qKhIKfkhxJTkl5RzenxLS3SseEBrbxf8MLY97AVWams0wpZzm2CaMxd7G7Tzc8Wy4W1gL7BSqYuhER1CtKN1svPGG2/gwIEDmD59OgAoEpwNGzZALBbrNzpC9ERoZ42nRVSgrC8hzTywPurf2alfvr1czsfVHjezCowZlsWYHuKv9JqKG9XiMBpCzJ/Wyc7ixYsRERGBtLQ0lJeXY8WKFUhLS8OZM2dU+u4QYio2jGmPQWtOcx2GxVj8VtBr15kVFoD46zSZp7b0PXEnIUSHAuUuXbogJSUF5eXlaNmyJY4cOQIPDw8kJSUhODjYEDESUm3/2Xae6xAsyoBVJzHn11S8kKhO/vskvxRdlhxD7+X0x48uqPkeIfqn9a3nlohuPbd8TT4/CImF1c2YitBAD6yPrLikFTQ/jvP6KHNFzfcI0Z6m399aX8aysrJCZmamSpHys2fP4OHhAalU9S89QrhGNTuGE5+WjQlbk3Hubg4lOq+w4QOH3u8OTzd7LD6YhnvPiuFXywGz+1bcNk7N9wgxDq1Hdvh8PrKyslSSnUePHqFRo0Z48eKFXgM0BhrZsXy3swrpsgoxGj4PuLEwgpIXQgxM7yM733//PYCKu6/kk4HKSaVS/Pnnn2jWrFk1QiZE/yTlMmxLuodNZ2hON2I8VHdDiGnRONlZtmwZAIAxhjVr1sDKykrxnEAggJ+fH9asWaPVwWNjYxEbG4t79+4BqGhMOHfuXERERAAASkpK8NFHH2HHjh0oLS1FWFgYVq9ejTp16ij2cezYMcyZMwepqalwdHREVFQUFi1aRPN1EcQcTMP6k+k0yzYxGqq7IcQ0aZwRyGc779mzJ/bu3QtXV9dqH9zLywtLlixB48aNwRjDli1bMHDgQFy+fBnNmzfHjBkzcODAAezatQsikQjTpk3D4MGDcfp0xS3EV65cQd++ffH5559j69atePjwISZPngypVIpvv/222vER8xVzMA1r/6TRHGIcTes4Ydgb3lR3Q4iJMrm7sdzc3LB06VIMHToU7u7u2L59O4YOHQoAuHHjBgICApCUlISOHTti9uzZiI+PR3JysmL7/fv3Y9iwYcjOzoazs7NGx6SaHcsiKZeh2ZxDNKJDjOb6l+GwF1i9fkVCiF4ZbCJQQ5FKpdixYweKioogFotx8eJFlJWVoXfv3op1mjVrBh8fHyQlJQEASktLlSYjBQB7e3uUlJTg4sWLlR6rtLQU+fn5Sv+I5diWdI8SHWI0oYEelOgQYuI4T3ZSU1Ph5OQEW1tbTJ48Gfv27UNgYCCysrIgEAjg4uKitH6dOnWQlZUFAAgLC8OZM2fw888/QyqV4uHDh/jyyy8BAJmZmZUeMyYmBiKRSPHP29vbYOdHjO9+TjHXIZAa4uUeQ4QQ08V5stO0aVOkpKTg3LlzmDJlCqKiopCWlqbRtn369MHSpUsxefJk2NraokmTJujbty+AilvkKxMdHY28vDzFvwcPHujlXIhp8HVz4DoEYsFaegoxpqMPrn8ZTokOIWaC82RHIBDA398fwcHBiImJQatWrbBixQrUrVsXEokEubm5Sus/fvwYdevWVTyeOXMmcnNzkZGRgadPn2LgwIEAgIYNVScmlLO1tYVQKFT6RyzHqA6+XIdALBAPQD2RHX6d2gULB7WkS1eEmBGtk53Dhw/j1KlTisf/+9//0Lp1a4waNQrPnz+vdkAymQylpaUIDg6GjY0Njh07pnju5s2byMjIUJldncfjoX79+rC3t8fPP/8Mb29vtG3bttqxEPMiKZdh48m7+GDHJa5DIRaG9///nde/ovMxIcS8aN2M5pNPPsHXX38NoKLe5qOPPsLMmTNx4sQJzJw5E5s2bdJ4X9HR0YiIiICPjw8KCgqwfft2JCQkIC4uDiKRCOPHj8fMmTPh5uYGoVCI6dOnQywWo2PHjop9LF26FOHh4eDz+di7dy+WLFmCX375RakPELF81FOHqGNrzUdpuUzxmM+DTp+RuiI7zOsfiPAW9fQYHSHEWLROdtLT0xEYWNEwa8+ePXjzzTexePFiXLp0SVEvo6ns7GxERkYiMzMTIpEIQUFBiIuLQ2hoKICKRoZ8Ph9DhgxRair4skOHDmHRokUoLS1Fq1at8NtvvymaEpKagXrq6EbXL35T16C2A7r418bsvoEQWPNxPj0H2QUl8HC2Q/sGbigsKce7m88j/VkRcjSYL21OvwCM7dyARnQIMWNa99lxc3PDqVOnEBgYiC5duiAyMhITJ07EvXv3EBgYiOJi87sThvrsmC/qqaMbPg/YO6kzBq05zXUoeqXNnFRSGUOXr48jK68E6j4+PFSM6JyaFUKJDiEmymB9drp06YKZM2di4cKFOH/+PPr16wcA+Pvvv+Hl5aV7xIRoSSpjWPjHNUp0dCBjwLQdlfeiMlfazEllxedhXv+KUepXUxmq0SHEsmid7KxatQrW1tbYvXs3YmNj4enpCaDiclJ4eLjeAyREncNXM9Hl6+PYdjaD61DMliaXcMwFnwdM6qb9nFThLeohdnRb1BUpNyetK7JD7Oi2VKNDiIUwuekiuECXsczL4auZmPLjJbWXHojmvFzs8E9uCddhaMzdWYDWXi5YNrwNBNZ8bEu6h/s5xfB1c6j2nFRSGVOp7aERHUJMn6bf31oXKF+6dAk2NjZo2bIlAOC3337Dpk2bEBgYiPnz50MgEOgeNSGvIZUxLNifRolONchrUWJHvYH+q0+9dn1TcXRGD4gcbBSPx3etvJeWtqz4PIgb1dLb/gghpkXrP4UmTZqEv//+GwBw9+5djBgxAg4ODti1axc+/fRTvQdIyMvOp+cgM898RiNMzcu1KOO2nOM0Fm19e+QG1yEQQsyU1snO33//jdatWwMAdu3ahW7dumH79u3YvHkz9uzZo+/4CFGSXUCJTnXUFdlh+YjWOH37KZ6aWc3OvWfmd6cnIcQ0aH0ZizEGmayiSdfRo0fx5ptvAgC8vb3x9OlT/UZHyCs8nO1evxJR4epgg9XvBGPjqTv4YEcK1+HoxK8WzXlGCNGN1iM7b7zxBr766its27YNiYmJilvP09PTUadOHb0HSMjL2jdwgyPNSaS1Ix92xw+n7+Lo9Sdch6Kz2VreaUUIIXJaJzvLly/HpUuXMG3aNHz++efw9/cHAOzevRudOnXSe4CEvEwqYyiWSLkOwyzFp2VzHYLObK351brbihBSs2l9GSsoKAipqakqy5cuXUrzURGD25Z0j+7E0sFbZnTXlTql5TKcT8+hO6YIITrR6U+l3NxcbNiwAdHR0cjJyQEApKWlITvbfP9yJObhfg4VqerCEhoIUnE6IURXWo/s/PXXX+jVqxdcXFxw7949TJgwAW5ubti7dy8yMjKwdetWQ8RJCADA142KVHXh5miD4lzzvvxHxemEEF1pPbIzc+ZMjBs3Drdu3YKd3b+/fPr27Ys///xTr8ER8qqQplQEr4slA4K4DkFnPAD1RBVdjQkhRBdaj+wkJydj7dq1Kss9PT2RlZWll6BIzfZCIsXig2m496wYfrUcMLtvIATWfJxPz8G4zee5Ds/sWPGA0VvN83WjCTkJIfqgdbJja2uL/Px8leV///033N3d9RIUqbkmbE1Wumvo5C1g29kMCKz5kJTLOIzMfEnNuKK7rsgO8/oH0oSchJBq0TrZGTBgAL788kv88ssvAAAej4eMjAzMmjULQ4YM0XuApOZ4NdF5GSU6Ncu0no3Q2d+dJuQkhOiF1jU7//3vf1FYWAgPDw+8ePEC3bt3h7+/P5ydnbFo0SJDxEhqgBcSqVn3gSH6Ia/PmRHaFOJGtSjRIYTohdYjOyKRCPHx8Th9+jSuXLmCwsJCtG3bFr179zZEfMTCSWUM59NzsOr4La5DIRyj+hxCiKHwGGNmfEVfP/Lz8yESiZCXlwehUMh1ODXG4auZWLA/jWYxJwAqRnSoPocQog1Nv7+1Htl5//334e/vj/fff19p+apVq3D79m0sX75c62BJzXP4aiam/HiJuiETtKgvxOf9Aqk+hxBiMFrX7OzZswedO3dWWd6pUyfs3r1bL0ERyyaVMSzYn0aJDgEA/PSfjlSfQwgxKK2TnWfPnkEkEqksFwqFePr0qV6CIpbtfHoOXboiCmmZqq0sCCFEn7ROdvz9/XH48GGV5YcOHULDhg31EhSxbDTHEXkZfR4IIYamdc3OzJkzMW3aNDx58gQhISEAgGPHjuG///0v1esQjdSkOY5srHgoM+eufkZQkz4PhBBuaJ3svPvuuygtLcWiRYuwcOFCAICfnx9iY2MRGRmp9wCJ5Wnt7cJ1CEazfngwxm6/wHUYJomHig7JNOcVIcTQqnXr+ZMnT2Bvbw8nJyd9xmR0dOu5cUjKZdiWdA+Hrmbiwv1crsMxOKGdNfJLyrkOwyTJS5FjR7elW80JIToz2K3nck+ePMHNmzcBAM2aNUPt2rV13RWpAWIOpmH9yXTIatAVHUp0KkdzXhFCjEnrZKeoqAjTp0/H1q1bIZNVzFdkZWWFyMhIrFy5Eg4ODnoPkpi3mINpWPtnOtdhGByfBzjaWqGgRMp1KBpxsrVCYalxYx3Uuj6Gt/OhnjqEEKPS+m6smTNnIjExEfv370dubi5yc3Px22+/ITExER999JEhYiRmTFIuw/qTlp/oAMDeSZ3NJtEBgLgPuhv1eHwe8M3QVtRThxBidFqP7OzZswe7d+9Gjx49FMv69u0Le3t7DBs2DLGxsfqMj5i5bUn3asylq/9sO891CBpzdxIg43mxUY85oWsDCKy1/vuKEEKqTevfPMXFxahTp47Kcg8PDxQXa/fLMzY2FkFBQRAKhRAKhRCLxTh06JDi+ZKSEkydOhW1atWCk5MThgwZgsePHyvtIzk5Gb169YKLiwtcXV0RFhaGK1euaHtaRM+kMoakO8+Q8PcTrkMxGnOp0XF3EiD5i1Cj9bfh84BJ3Rogum+gUY5HCCGv0jrZEYvFmDdvHkpK/v1F+eLFCyxYsABisVirfXl5eWHJkiW4ePEiLly4gJCQEAwcOBDXrl0DAMyYMQP79+/Hrl27kJiYiEePHmHw4MGK7QsLCxEeHg4fHx+cO3cOp06dgrOzM8LCwlBWVqbtqRE9OXw1E12+Po6R68/i5K2a01VbaKdzvb/B2Vjx0MTDEZe+CEXyF6EANO9vU50LTmM6+uDGwghKdAghnNL61vPU1FSEh4ejtLQUrVq1AgBcuXIFdnZ2iIuLQ/PmzasVkJubG5YuXYqhQ4fC3d0d27dvx9ChQwEAN27cQEBAAJKSktCxY0dcuHAB7dq1Q0ZGBry9vRXxBQUF4datW/D399fomHTruf7U5Ak+z37WCx2XHOM6DLWufxkOe4GV0jKpjKHL18eRlVei9v2S98FJ/KQnui89Uel66si3PTUrhOpzCCEGo+n3t9YjOy1btsStW7cQExOD1q1bo3Xr1liyZAlu3bpVrURHKpVix44dKCoqglgsxsWLF1FWVobevXsr1mnWrBl8fHyQlJQEAGjatClq1aqFjRs3QiKR4MWLF9i4cSMCAgLg5+encyxENzV5gs8gL6FKMmFK8opVRzqt+DzM618x4vJqOiJ/PK9/IATW/ErXU+flbSnRIYSYAq3G3cvKytCsWTP88ccfmDBhgl4CSE1NhVgsRklJCZycnLBv3z4EBgYiJSUFAoEALi4uSuvXqVMHWVlZAABnZ2ckJCRg0KBBim7OjRs3RlxcHKytKz+10tJSlJaWKh7n59NEhPpQUyf4bFFfiDrOduiwOJ7rUCr15so/cWFOH5Xl4S3qIXZ0WyzYn6b03r3aB6ey9VwdbMAA5L6UTFEPHUKIqdEq2bGxsVGq1dGHpk2bIiUlBXl5edi9ezeioqKQmJio0bYvXrzA+PHj0blzZ/z888+QSqX49ttv0a9fPyQnJ8Pe3l7tdjExMViwYIE+T4OgZk3oWE9khxb1hXj4vBBXH+Xj6iPTTpirKp4Ob1EPoYF1cT49B9kFJfBwtlPbB6ey9QC8dltCCOGS1jU7ixcvxt9//40NGzZUOXqiq969e6NRo0YYPnw4evXqhefPnyuN7vj6+uLDDz/EjBkzsHHjRsyePRuZmZng8yuuyEkkEri6umLjxo0YMWKE2mOoG9nx9vammp1qSrrzDCPXn+U6DKP4eUJHxBxKw1//mHaSI1fb0UbtyA4hhJgzg00XkZycjGPHjuHIkSNo2bIlHB0dlZ7fu3ev9tG+RCaTobS0FMHBwbCxscGxY8cwZMgQAMDNmzeRkZGhuOuruLgYfD4fPN6/f0XKH8u7O6tja2sLW1vbasVJVLVv4IZ6IjutClnNjbzwNrCe0GwSHQDYO6UL1yEQQghntE52XFxcFMlHdUVHRyMiIgI+Pj4oKCjA9u3bkZCQgLi4OIhEIowfPx4zZ86Em5sbhEIhpk+fDrFYjI4dOwIAQkND8cknn2Dq1KmYPn06ZDIZlixZAmtra/Ts2VMvMRLNyQtep/x4CTzA4hKelwtvP96VwmUoWou/noXxXRtyHQYhhHBC62Rn06ZNejt4dnY2IiMjkZmZCZFIhKCgIMTFxSE0tKIPyLJly8Dn8zFkyBCUlpYiLCwMq1evVmzfrFkz7N+/X9Hjh8/no02bNjh8+DDq1aPiSC5UVshqCV4uvF129BbX4Wjlfo5xuyUTQogp0bhmRyaTYenSpfj9998hkUjQq1cvzJs3r9IiYHNCfXb0TypjOJ+egw93Xsbj/NLXb2DCpvX0R2f/2kqFtxO2JCP+ejbHkWluTr8AGtkhhFgcvffZWbRoEWbPng0nJyd4enpixYoVmDp1ql6CJZbHis+DuFEtrBzWlutQdMZDxV1XM0KbqExeObV7Y+4C0xKfB4wR+3EdBiGEcEbjy1hbt27F6tWrMWnSJADA0aNH0a9fP2zYsEFxJxQhcoUl5Zix87JZjX68rLLGePIRq3c2mM9dZzQBJyGkptM42cnIyEDfvn0Vj3v37g0ej4dHjx7By8vLIMER8zRg1UmzulMJqBj9eHl2dnWN8Q5fzTSrWiQ+ryLRoXmpCCE1ncbJTnl5OezslCcOtLGxoQk3iRJzSnTsbHjwcXXAjomdIHKwqbIxnjnN+dUnsA46NHDDGLEfjegQQgi0SHYYYxg7dqxSf5qSkhJMnjxZqddOdfvsEPNVWFJuNokOnwf8NS9cKRkQN6qldl1zmvOLzwNWjWpLSQ4hhLxE42QnKipKZdno0aP1GgwxT3nFZXh383mkPszjOhQAgDUfGN+lAdb+mV7pOtrUsZjTnF8yBly8/7zSxI0QQmoijZMdffbXIZaj+9LjuP/sBddhKFjzgduL+ykerz+ZrlSLo0sdi7nN+WVu8RJCiKHpf3IrUmOYUqLjYMPH4Q+6w6e2g2JZdN9AfNSnGbYl3cP9nGL4ujnoVMfi4Wz3+pVMiLnFSwghhkbJDtFJXnGZySQ6J2b2QAMPR7XPCaz51W6m19JTVK3tjUU+b5d8JnJCCCEVqIqRaCWnUII+3yWgzZdHuA5F4Yczdw26/68PXzfo/vWhsr5AhBBCaGSHaKHdV/F4UijhOgwV954Zdt4nQ+9fH9T1BSKEEFKBkh2iEVNNdADAr5bD61eq5v5Pmui8n9N6NkJnf3eVvkCEEEL+RZexyGvlFEpMNtEBgNkG7hA8KzzAoPvXxb/zdjVVmbeLEEKIMhrZIZXKKZRgxLozuJVdxHUolQoN9IC9wMqgxzCV/kFyVJ9DCCHaoWSHqGXKl63kQgM9sD6yncGPY2p9a6g+hxBCtEPJDlFhyomOs60VBrXxxOy+gQYf0ZEzlb41VJ9DCCG6oWSHKDH1+pzET0Lg5iQw6jG57rMj758zI7QpJTmEEKIDKlAmSkasO8N1CFW6+bjA6Mfkss8O1ecQQkj10cgOAfDvZJ5/m3AxMsBN/QyXfXaoPocQQqqPkh1iUnNcvQ4X9TNc9dkJDfDAmjFv0IgOIYRUE13GquHMJdGR95XhYt4nQ/fxqcz3I9tSokMIIXpAyU4NZkqTeValJtatGKN/ECGE1BR0GauGkpTL0O/7P7kOQy2BNR+ScpniMVd1K5JyGbYl3cPOCxlGPa6x+gcRQkhNQclODRRzMA3rT6ZDxrg5fj2RHVrUF2LZ8Daw4vOw+GAa7j0rhl8tB8zuGwiBNR/n03OQXVACD2c7TvrKGPs1alrXGe39XI3aP4gQQmoKHmOMo68805Gfnw+RSIS8vDwIhUKuwzGomINpWPtnOqcx/DyhI8SNanEaQ1W4eI1M/TUhhBBTpOn3N43s1CCSchnWn+Qu0ZE3x+OiyFhTxn6NzOE1IYQQc0cFyjXItqR7nF26MpciY2O+RubymhBCiLmjZKcGuZ/DbXO82NFtTb45niFfI+tXEhpzeU0IIcTc0WWsGsTXzcHoxzS3ySv1/Ro52vDRyb82lg1vA3uBFeeF14QQUhNRslODDGjliYUHjDPPk7lOXjk02Fuvr9G5z0PhZPfvjxkVIRNCiPHRZawaZPKPF4xyHHOuRfn2yA297SvIS6iU6BBCCOEGp8lObGwsgoKCIBQKIRQKIRaLcejQIcXzJSUlmDp1KmrVqgUnJycMGTIEjx8/Vjy/efNm8Hg8tf+ys7O5OCWT9ijPOJNomnMtir4m/QzyEuL3aV31si9CCCHVw+mfnV5eXliyZAkaN24Mxhi2bNmCgQMH4vLly2jevDlmzJiBAwcOYNeuXRCJRJg2bRoGDx6M06dPAwCGDx+O8PBwpX2OHTsWJSUl8PDw4OKUTFp9kR0yDZjwmFt9jjrVmfTT3UmA1t4uWDa8DY3oEEKICTG5poJubm5YunQphg4dCnd3d2zfvh1Dhw4FANy4cQMBAQFISkpCx44dVbZ98uQJPD09sXHjRowZM0bjY9aUpoI5hRK0/Spe7/uV1+ecmhVitkmO3AuJFAFzD2u9HZ8H3FgYAYE1XRkmhBBj0fT722R+M0ulUuzYsQNFRUUQi8W4ePEiysrK0Lt3b8U6zZo1g4+PD5KSktTuY+vWrXBwcFAkR5UpLS1Ffn6+0r+a4ObjAr3v05zrc9SxF1ghNFD7UcEJXRtQokMIISaK89/OqampcHJygq2tLSZPnox9+/YhMDAQWVlZEAgEcHFxUVq/Tp06yMrKUruvjRs3YtSoUbC3t6/ymDExMRCJRIp/3t7e+jodk5ZdUP1LWK+mM+Zcn1OZ9ZHtNE54+DxgUrcGiO4baOCoCCGE6IrzwoKmTZsiJSUFeXl52L17N6KiopCYmKj1fpKSknD9+nVs27bttetGR0dj5syZisf5+fk1IuHxcLbTaTtrHtDQ3RE7JnaCyMGmRvSKWR/ZDi8kUpVJSq34PGxLuof7OcXwdXPAGLEfjegQQoiJ4zzZEQgE8Pf3BwAEBwcjOTkZK1aswPDhwyGRSJCbm6s0uvP48WPUrVtXZT8bNmxA69atERwc/Npj2trawtbWVm/nYC5ae7votN35z0Ph5iRQPK4pvWLsBVZYOKilyvLxXRtyEA0hhBBdmdyfpDKZDKWlpQgODoaNjQ2OHTumeO7mzZvIyMiAWCxW2qawsBC//PILxo8fb+xwzcr2c/d12m7f5X/0HAkhhBBiPJyO7ERHRyMiIgI+Pj4oKCjA9u3bkZCQgLi4OIhEIowfPx4zZ86Em5sbhEIhpk+fDrFYrHIn1s6dO1FeXo7Ro0dzdCbmQdd5n7icU4sQQgipLk6TnezsbERGRiIzMxMikQhBQUGIi4tDaGgoAGDZsmXg8/kYMmQISktLERYWhtWrV6vsZ+PGjRg8eLBKMTNRpuu8T1zMqUUIIYToi8n12eFCTemzo2sPmStz+0DkYGOAiAghhBDdafr9zXmBMtEvqYyp3C0lKZdh8cE0pDzI1Wmf3x65obZQlxBCCDEHlOxYkMNXM7Fgf5rSlBC21nyUlsuqtV99zRdFCCGEcIGSHQtx+Gompvx4Ca9ek6xuogNUzBdFCCGEmCuTu/WcaE8qY1iwP00l0dGX2dQdmBBCiBmjZMcCnE/PMdhs5qGBHrAXWBlk34QQQogx0GUsM1ZYUo4ZOy/j6iPDTGQaGuiB9ZHtDLJvQgghxFgo2TFTA1adxF//GCjJCfDA9yPb0ogOIYQQi0DJjhkyVKLDQ8Us5mvGvGGRk3sSQgipmahmx8wUlpQbLNEBgHn9AynRIYQQYlFoZMdMyJsFLjqQZpD91xXZYV7/QIS3qGeQ/RNCCCFcoWTHDKhrFqgvTes6Y37/5mjfwI1GdAghhFgkSnZMXGXNAvWlvZ8rxI1qGWjvhBBCCPeoZseEGbpZIEANAwkhhFg+SnZMmCGbBQLUMJAQQkjNQMmOCcsuMGyiQw0DCSGE1ARUs2PCPJzt9Lq/lp5CtPZ2wey+gTSiQwghpMagZMeEBfu6gs8DZHoq2vllUidKcgghhNQ4dBnLBBWWlGPClmT0+m+C3hIdANh+7r7+dkYIIYSYCRrZMTGGnPPqfk6xQfZLCCGEmDIa2TEhhkx0AMDXzcFg+yaEEEJMFSU7JsJQc17J8XnAGLGfwfZPCCGEmCq6jMUxQ895JTehawMIrCm3JYQQUvNQssMhQ855JcfnVSQ60dQpmRBCSA1FyQ5HDD3nVZM6Thj+hjfGiP1oRIcQQkiNRskOB4wx59VvU7tQTx1CCCEEVKDMCZrzihBCCDEeSnY4QHNeEUIIIcZDl7E4oO85r6z4wKj2PjTnFSGEEKIGJTscCKwn1Ov+TszsCZ/a1DCQEEIIUYcuY3Hg410pet1f/PUsve6PEEIIsSScJjuxsbEICgqCUCiEUCiEWCzGoUOHFM+XlJRg6tSpqFWrFpycnDBkyBA8fvxYZT+bN29GUFAQ7Ozs4OHhgalTpxrzNLSW8fyFXvdHc14RQgghleM02fHy8sKSJUtw8eJFXLhwASEhIRg4cCCuXbsGAJgxYwb279+PXbt2ITExEY8ePcLgwYOV9vHdd9/h888/x2effYZr167h6NGjCAsL4+J0NObjaq/X/dGcV4QQQkjleIwxQ7Z70ZqbmxuWLl2KoUOHwt3dHdu3b8fQoUMBADdu3EBAQACSkpLQsWNHPH/+HJ6enti/fz969eql8zHz8/MhEomQl5cHoVC/9TTq3M4qRO/liXrZF58H3FgYQY0DCSGE1Diafn+bzDekVCrFjh07UFRUBLFYjIsXL6KsrAy9e/dWrNOsWTP4+PggKSkJABAfHw+ZTIaHDx8iICAAXl5eGDZsGB48eFDlsUpLS5Gfn6/0z5hGrD+jt33RnFeEEEJI1Tj/lkxNTYWTkxNsbW0xefJk7Nu3D4GBgcjKyoJAIICLi4vS+nXq1EFWVkVB7t27dyGTybB48WIsX74cu3fvRk5ODkJDQyGRSCo9ZkxMDEQikeKft7e3IU9RQSpjSLrzDM+Ly6q9Lz4PmNSN5rwihBBCXofzW8+bNm2KlJQU5OXlYffu3YiKikJiomaXeGQyGcrKyvD999+jT58+AICff/4ZdevWxYkTJyqt3YmOjsbMmTMVj/Pz8w2e8Ohr0k+hnRU+6NWE5rwihBBCNMR5siMQCODv7w8ACA4ORnJyMlasWIHhw4dDIpEgNzdXaXTn8ePHqFu3LgCgXr16AIDAwH9HN9zd3VG7dm1kZGRUekxbW1vY2toa4GzU0+eknwkfh8DNSaCHPRFCCCE1g8kNDchkMpSWliI4OBg2NjY4duyY4rmbN28iIyMDYrEYANC5c2fFcrmcnBw8ffoUvr6+xg28Evqc9NPdSUCJDiGEEKIlTkd2oqOjERERAR8fHxQUFGD79u1ISEhAXFwcRCIRxo8fj5kzZ8LNzQ1CoRDTp0+HWCxGx44dAQBNmjTBwIED8cEHH2DdunUQCoWIjo5Gs2bN0LNnTy5PDVIZw/n0HJy+/VQvk366OwmQ/EWoHiIjhBBCahZOk53s7GxERkYiMzMTIpEIQUFBiIuLQ2hoxZf6smXLwOfzMWTIEJSWliIsLAyrV69W2sfWrVsxY8YM9OvXD3w+H927d8fhw4dhY2PDxSkB0F99DgA08XDEjomdaESHEEII0ZHJ9dnhgj777OizPifYxwV73uushz0RQgghlsfs+uxYAn3W5wDAD2Pb62lPhBBCSM1FyY4enU/P0culKwDwrWUPkQN3l+IIIYQQS0HJjh5lF+gv0Un8JEQv+yKEEEJqOs777FgSD2c7nbd1trNCEw9n/DC2PY3oEEIIIXpEyY4etfZ20Wk7Pg+4+EUf6ohMCCGEGAB9u+rR9nP3ddqOJvMkhBBCDIdGdvTofk6xVuvzeRWJDk3mSQghhBgOJTt65OvmoNF6b/i6IKJFPZrMkxBCCDEC+qbVozFiP/B5Va/D5wHbJ4gxvmtDSnQIIYQQI6BvWz0SWPMxoWuDKteh+hxCCCHEuOgylp7J62/Wn0yH7KVWylSfQwghhHCD5saCfufGkpOUy7At6R7u5xTD182B6nMIIYQQPdP0+5tGdgxEYM3H+K4NuQ6DEEIIqfFoqIEQQgghFo2SHUIIIYRYNEp2CCGEEGLRKNkhhBBCiEWjZIcQQgghFo2SHUIIIYRYNEp2CCGEEGLRKNkhhBBCiEWjZIcQQgghFo06KAOQz5iRn5/PcSSEEEII0ZT8e/t1M19RsgOgoKAAAODt7c1xJIQQQgjRVkFBAUQiUaXP00SgAGQyGR49egRnZ2fweDyuwzGI/Px8eHt748GDB3qb7JRoj94H00Dvg2mg98E0mPP7wBhDQUEB6tevDz6/8socGtkBwOfz4eXlxXUYRiEUCs3uw2yJ6H0wDfQ+mAZ6H0yDub4PVY3oyFGBMiGEEEIsGiU7hBBCCLFolOzUELa2tpg3bx5sbW25DqVGo/fBNND7YBrofTANNeF9oAJlQgghhFg0GtkhhBBCiEWjZIcQQgghFo2SHUIIIYRYNEp2CCGEEGLRKNkxUzExMWjXrh2cnZ3h4eGBQYMG4ebNm0rr3LlzB2+99Rbc3d0hFAoxbNgwPH78WGVfBw4cQIcOHWBvbw9XV1cMGjTISGdh/vT1Pvz9998YOHAgateuDaFQiC5duuDEiRPGPBWzFxsbi6CgIEVjNLFYjEOHDimeLykpwdSpU1GrVi04OTlhyJAhKu9DRkYG+vXrBwcHB3h4eOCTTz5BeXm5sU/FrFX3fbhy5QpGjhwJb29v2NvbIyAgACtWrODiVMyaPn4e5J49ewYvLy/weDzk5uYa6Qz0i5IdM5WYmIipU6fi7NmziI+PR1lZGfr06YOioiIAQFFREfr06QMej4fjx4/j9OnTkEgk6N+/P2QymWI/e/bswZgxYzBu3DhcuXIFp0+fxqhRo7g6LbOjr/fhzTffRHl5OY4fP46LFy+iVatWePPNN5GVlcXVqZkdLy8vLFmyBBcvXsSFCxcQEhKCgQMH4tq1awCAGTNmYP/+/di1axcSExPx6NEjDB48WLG9VCpFv379IJFIcObMGWzZsgWbN2/G3LlzuTols1Td9+HixYvw8PDAjz/+iGvXruHzzz9HdHQ0Vq1axdUpmaXqvg8vGz9+PIKCgowZvv4xYhGys7MZAJaYmMgYYywuLo7x+XyWl5enWCc3N5fxeDwWHx/PGGOsrKyMeXp6sg0bNnASsyXS5X148uQJA8D+/PNPxTr5+fkMgGIdohtXV1e2YcMGlpuby2xsbNiuXbsUz12/fp0BYElJSYwxxg4ePMj4fD7LyspSrBMbG8uEQiErLS01euyWRJv3QZ333nuP9ezZ0xihWjRd3ofVq1ez7t27s2PHjjEA7Pnz50aOWj9oZMdC5OXlAQDc3NwAAKWlpeDxeEpNouzs7MDn83Hq1CkAwKVLl/Dw4UPw+Xy0adMG9erVQ0REBK5evWr8E7AQurwPtWrVQtOmTbF161YUFRWhvLwca9euhYeHB4KDg41/EhZAKpVix44dKCoqglgsxsWLF1FWVobevXsr1mnWrBl8fHyQlJQEAEhKSkLLli1Rp04dxTphYWHIz89X/DVMtKPL+6BOXl6e4meKaE/X9yEtLQ1ffvkltm7dWuUkm+bAvKMnACpmbf/www/RuXNntGjRAgDQsWNHODo6YtasWSguLkZRURE+/vhjSKVSZGZmAgDu3r0LAJg/fz6++OIL/PHHH3B1dUWPHj2Qk5PD2fmYK13fBx6Ph6NHj+Ly5ctwdnaGnZ0dvvvuOxw+fBiurq5cnpLZSU1NhZOTE2xtbTF58mTs27cPgYGByMrKgkAggIuLi9L6derUUVwqzMrKUkp05M/LnyOaq8778KozZ85g586dmDhxohEityzVeR9KS0sxcuRILF26FD4+PhxEr1+U7FiAqVOn4urVq9ixY4dimbu7O3bt2oX9+/fDyckJIpEIubm5aNu2rSJDl9eMfP755xgyZAiCg4OxadMm8Hg87Nq1i5NzMWe6vg+MMUydOhUeHh44efIkzp8/j0GDBqF///6KhIhopmnTpkhJScG5c+cwZcoUREVFIS0tjeuwahx9vQ9Xr17FwIEDMW/ePPTp08cAkVq26rwP0dHRCAgIwOjRow0cpZFwfR2NVM/UqVOZl5cXu3v3bqXrPHnyRHGdtU6dOuybb75hjDF2/PhxBoCdPHlSaf327duz2bNnGyxmS1Sd9+Ho0aMqdT2MMebv789iYmIMFnNN0KtXLzZx4sRK6w18fHzYd999xxhjbM6cOaxVq1ZKz9+9e5cBYJcuXTJSxJZJm/dB7tq1a8zDw4N+F+mRNu9Dq1atGJ/PZ1ZWVszKyorx+XwGgFlZWbG5c+dyEH310MiOmWKMYdq0adi3bx+OHz+OBg0aVLpu7dq14eLiguPHjyM7OxsDBgwAAAQHB8PW1lbpVumysjLcu3cPvr6+Bj8HS6CP96G4uBgAVK6J8/l8pTu2iPZkMhlKS0sRHBwMGxsbHDt2TPHczZs3kZGRAbFYDAAQi8VITU1Fdna2Yp34+HgIhUIEBgYaPXZLos37AADXrl1Dz549ERUVhUWLFnERskXS5n3Ys2cPrly5gpSUFKSkpGDDhg0AgJMnT2Lq1KmcxF8tXGdbRDdTpkxhIpGIJSQksMzMTMW/4uJixTo//PADS0pKYrdv32bbtm1jbm5ubObMmUr7+eCDD5inpyeLi4tjN27cYOPHj2ceHh4sJyfH2KdklvTxPjx58oTVqlWLDR48mKWkpLCbN2+yjz/+mNnY2LCUlBQuTsssffbZZywxMZGlp6ezv/76i3322WeMx+OxI0eOMMYYmzx5MvPx8WHHjx9nFy5cYGKxmInFYsX25eXlrEWLFqxPnz4sJSWFHT58mLm7u7Po6GiuTsksVfd9SE1NZe7u7mz06NFKP1PZ2dlcnZJZqu778KoTJ06Y9d1YlOyYKQBq/23atEmxzqxZs1idOnWYjY0Na9y4Mfvvf//LZDKZ0n4kEgn76KOPmIeHB3N2dma9e/dmV69eNfLZmC99vQ/JycmsT58+zM3NjTk7O7OOHTuygwcPGvlszNu7777LfH19mUAgYO7u7qxXr16KX+yMMfbixQv23nvvMVdXV+bg4MDeeustlpmZqbSPe/fusYiICGZvb89q167NPvroI1ZWVmbsUzFr1X0f5s2bp/ZnytfXl4OzMV/6+Hl4mbknOzzGGDPyYBIhhBBCiNFQzQ4hhBBCLBolO4QQQgixaJTsEEIIIcSiUbJDCCGEEItGyQ4hhBBCLBolO4QQQgixaJTsEEIIIcSiUbJDSA2WkJAAHo+H3NxcrkMhVdi8eTN4PB54PB4+/PBDrbbt0aOHYtuUlBSDxEeIqaNkhxALl5SUBCsrK/Tr10/luU6dOiEzMxMikUirfda0JInH4+HXX3/lNAahUIjMzEwsXLhQsaxHjx4qyc+KFStga2uLHTt2AAD27t2L8+fPGzNUQkwOJTuEWLiNGzdi+vTp+PPPP/Ho0SOl5wQCAerWrQsej6d2W6lUarGTkXJxbhKJROdteTwe6tatC2dn50rXmTdvHmbPno3ffvsNI0aMAAC4ubnB3d1d5+MSYgko2SHEghUWFmLnzp2YMmUK+vXrh82bNys9/+oIzebNm+Hi4oLff/8dgYGBsLW1RUZGhtI29+7dQ8+ePQEArq6u4PF4GDt2LICKWZVjYmLQoEED2Nvbo1WrVti9e7fK8eLi4tCmTRvY29sjJCQE2dnZOHToEAICAiAUCjFq1CjFbPBAxQjGtGnTMG3aNIhEItSuXRtz5szBy7PdlJaW4uOPP4anpyccHR3RoUMHJCQkKJ6v7NySk5MRGhqK2rVrQyQSoXv37rh06ZJiOz8/PwDAW2+9BR6Pp3g8duxYDBo0SOm1+fDDD9GjRw+VuD/88EPUrl0bYWFhAICrV68iIiICTk5OqFOnDsaMGYOnT59W+V5WhTGG6dOn4/vvv0d8fDzCw8N13hchloiSHUIs2C+//IJmzZqhadOmGD16NH744Qe8bjq84uJifP3119iwYQOuXbsGDw8Ppee9vb2xZ88eAMDNmzeRmZmJFStWAABiYmKwdetWrFmzBteuXcOMGTMwevRoJCYmKu1j/vz5WLVqFc6cOYMHDx5g2LBhWL58ObZv344DBw7gyJEjWLlypdI2W7ZsgbW1Nc6fP48VK1bgu+++w4YNGxTPT5s2DUlJSdixYwf++usvvP322wgPD8etW7eqPLeCggJERUXh1KlTOHv2LBo3boy+ffuioKAAAJCcnAwA2LRpEzIzMxWPNbVlyxYIBAKcPn0aa9asQW5uLkJCQtCmTRtcuHABhw8fxuPHjzFs2DCt9itXXl6O0aNHY/fu3UhMTESnTp102g8hFo3TaUgJIQbVqVMntnz5csYYY2VlZax27drsxIkTiudfncl406ZNDABLSUmpcr/qZkAuKSlhDg4O7MyZM0rrjh8/no0cOVJpu6NHjyqej4mJYQDYnTt3FMsmTZrEwsLCFI+7d+/OAgIClGaLnzVrFgsICGCMMXb//n1mZWXFHj58qHTsXr16sejoaK3OTSqVMmdnZ7Z//37FMgBs3759SutFRUWxgQMHKi374IMPWPfu3ZXibtOmjdI6CxcuZH369FFa9uDBAwaA3bx5U21MmzZtYiKRSGV59+7dmUAgYAKBgF2/fr3Sc0pPT2cA2OXLlytdhxBLRiM7hFiomzdv4vz58xg5ciQAwNraGsOHD8fGjRur3E4gECAoKEjr492+fRvFxcUIDQ2Fk5OT4t/WrVtx584dpXVf3n+dOnXg4OCAhg0bKi3Lzs5W2qZjx45KtUVisRi3bt2CVCpFamoqpFIpmjRponTsxMREpWOrO7fHjx9jwoQJaNy4MUQiEYRCIQoLC1Uu3+kqODhY6fGVK1dw4sQJpTibNWsGACqvkya6dOkCJycnzJkzB+Xl5XqJmRBLY811AIQQw9i4cSPKy8tRv359xTLGGGxtbbFq1apK78Cyt7evtGC5KoWFhQCAAwcOwNPTU+k5W1tbpcc2NjaK/+fxeEqP5cu0KR4uLCyElZUVLl68CCsrK6XnnJycFP+v7tyioqLw7NkzrFixAr6+vrC1tYVYLH5tMTGfz1e5JFhWVqaynqOjo0qs/fv3x9dff62ybr169ao8pjotW7bEf//7X/Tu3RvDhw/Hzp07YW1Nv9oJeRn9RBBigcrLy7F161b897//RZ8+fZSeGzRoEH7++WdMnjxZ5/0LBAIAFXc0yb1c9Nu9e3ed912Zc+fOKT2W19dYWVmhTZs2kEqlyM7ORteuXbXa7+nTp7F69Wr07dsXAPDgwQOVYmEbGxulcwUAd3d3XL16VWlZSkqKSuL2qrZt22LPnj3w8/PTW1LSunVrHDt2DL1798awYcOwc+fO18ZBSE1Cl7EIsUB//PEHnj9/jvHjx6NFixZK/4YMGfLaS1mv4+vrCx6Phz/++ANPnjxBYWEhnJ2d8fHHH2PGjBnYsmUL7ty5g0uXLmHlypXYsmVLtc8pIyMDM2fOxM2bN/Hzzz9j5cqV+OCDDwAATZo0wTvvvIPIyEjs3bsX6enpOH/+PGJiYnDgwIEq99u4cWNs27YN169fx7lz5/DOO+/A3t5eaR0/Pz8cO3YMWVlZeP78OQAgJCQEFy5cwNatW3Hr1i3MmzdPJflRZ+rUqcjJycHIkSORnJyMO3fuIC4uDuPGjVNJqLTRqlUrHD9+HKdOncKwYcPUjjIRUlNRskOIBdq4cSN69+6t9lLVkCFDcOHCBfz1118679/T0xMLFizAZ599hjp16mDatGkAgIULF2LOnDmIiYlBQEAAwsPDceDAATRo0EDnY8lFRkbixYsXaN++PaZOnYoPPvgAEydOVDy/adMmREZG4qOPPkLTpk0xaNAgJCcnw8fHp8r9bty4Ec+fP0fbtm0xZswYvP/++yp3oP33v/9FfHw8vL290aZNGwBAWFgY5syZg08//RTt2rVDQUEBIiMjX3se9evXx+nTpyGVStGnTx+0bNkSH374IVxcXMDnV+9XcsuWLXH8+HGcOXMGb7/9drX6+hBiSXjs1YvOhBBiYnr06IHWrVtj+fLlXIfCic2bN+PDDz/UuWP1vXv30KBBA1y+fBmtW7fWa2yEmAMa2SGEEDOQl5cHJycnzJo1S6vtIiIi0Lx5cwNFRYh5oAJlQggxcUOGDEGXLl0AAC4uLlptu2HDBrx48QIAXntJjxBLRZexCCGEEGLR6DIWIYQQQiwaJTuEEEIIsWiU7BBCCCHEolGyQwghhBCLRskOIYQQQiwaJTuEEEIIsWiU7BBCCCHEolGyQwghhBCLRskOIYQQQiza/wHNh4/jIf+0JgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Air temperature [K]'], data['Process temperature [K]'])\n",
    "plt.xlabel('Air temperature [K]')\n",
    "plt.ylabel('Process temperature [K]')\n",
    "plt.title('Scatter Plot: Air temperature vs. Process temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plot for numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "data.boxplot()\n",
    "plt.title('Box Plot of Numerical Features')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2: Machine Learning Mastery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.1: Overview different machine learning algorithms and select appropriate ones for predictive maintenance (e.g., regression, decision trees, SVM)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I conducted research to understand various machine learning algorithms commonly used in predictive maintenance applications. I explored several types of algorithms, including regression, decision trees, support vector machines (SVM), clustering algorithms, and anomaly detection algorithms.\n",
    "\n",
    "a. Regression Algorithms:\n",
    "\n",
    "* Linear Regression: A simple and interpretable algorithm for modeling linear relationships between input features and the target variable.\n",
    "* Ridge Regression and Lasso Regression: Variants of linear regression that include regularization to prevent overfitting and improve generalization performance.\n",
    "\n",
    "b. Decision Trees and Ensemble Methods:\n",
    "\n",
    "* Decision Trees: Non-linear models that recursively partition the featire space to make predictions.\n",
    "* Random Forest and Gradient Boosting Machines (GBM): Ensemble methods that combine multiple decision trees to improve prediction accuracy and handle complex relationships in the data.\n",
    "\n",
    "c. Support Vector Machines (SVM):\n",
    "\n",
    "* SVM: A supervised learning algorithm that finds the optimal hyperplane to separate data points into different classes, suitable for both classification and regression tasks.\n",
    "\n",
    "d. Clustering Algorithm:\n",
    "\n",
    "* K-means: An unsupervised learning algorithm used for clustering data into groups based on similarity.\n",
    "* DBSCAN: Another clustering algorithm that can identify clusters of arbitrary shapes and handle noise in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Implement supervised learning to predict time-to-failure and unsupervised learning to detect anomalous behavior in machine performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: (Data is already loaded and preprocessed previously)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UDI</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Type</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>Target</th>\n",
       "      <th>Failure Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>M14860</td>\n",
       "      <td>M</td>\n",
       "      <td>298.1</td>\n",
       "      <td>308.6</td>\n",
       "      <td>1551</td>\n",
       "      <td>42.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>L47181</td>\n",
       "      <td>L</td>\n",
       "      <td>298.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>1408</td>\n",
       "      <td>46.3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>L47182</td>\n",
       "      <td>L</td>\n",
       "      <td>298.1</td>\n",
       "      <td>308.5</td>\n",
       "      <td>1498</td>\n",
       "      <td>49.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>L47183</td>\n",
       "      <td>L</td>\n",
       "      <td>298.2</td>\n",
       "      <td>308.6</td>\n",
       "      <td>1433</td>\n",
       "      <td>39.5</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>L47184</td>\n",
       "      <td>L</td>\n",
       "      <td>298.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>1408</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>No Failure</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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]  Target Failure Type  \n",
       "0                    1551         42.8                0       0   No Failure  \n",
       "1                    1408         46.3                3       0   No Failure  \n",
       "2                    1498         49.4                5       0   No Failure  \n",
       "3                    1433         39.5                7       0   No Failure  \n",
       "4                    1408         40.0                9       0   No Failure  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encode categorical variables using one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-numeric columns found. Dropping: Index(['Product ID_H29424', 'Product ID_H29425', 'Product ID_H29432',\n",
      "       'Product ID_H29434', 'Product ID_H29441', 'Product ID_H29452',\n",
      "       'Product ID_H29457', 'Product ID_H29462', 'Product ID_H29466',\n",
      "       'Product ID_H29481',\n",
      "       ...\n",
      "       'Product ID_M24846', 'Product ID_M24847', 'Product ID_M24849',\n",
      "       'Product ID_M24851', 'Product ID_M24855', 'Product ID_M24857',\n",
      "       'Product ID_M24859', 'Type_H', 'Type_L', 'Type_M'],\n",
      "      dtype='object', length=10003)\n",
      "Shape of X: (10000, 7)\n",
      "Shape of y: (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv('Machine Predictive Maintenance Classification\\predictive_maintenance.csv')\n",
    "data.head()\n",
    "\n",
    "X = data[['Product ID', 'Type', 'Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']]\n",
    "y = data['Target']\n",
    "\n",
    "# Encoding categorical variables in X\n",
    "X_encoded = pd.get_dummies(X, columns=['Product ID', 'Type'])\n",
    "\n",
    "# Identifying and dropping non-numeric columns\n",
    "non_numeric_columns = X_encoded.select_dtypes(exclude=['number']).columns\n",
    "if len(non_numeric_columns) > 0:\n",
    "    print(\"Non-numeric columns found. Dropping:\", non_numeric_columns)\n",
    "    X_encoded = X_encoded.drop(columns=non_numeric_columns)\n",
    "else:\n",
    "    print(\"No non-numeric columns found.\")\n",
    "\n",
    "# Drop non-numeric columns\n",
    "X_encoded = X_encoded.select_dtypes(include=['number'])\n",
    "\n",
    "# Align X_encoded and y\n",
    "X_encoded, y = X_encoded.align(y, axis=0)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Check the shape of X and y\n",
    "print(\"Shape of X:\", X.shape)\n",
    "print(\"Shape of y:\", y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9835\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1939\n",
      "           1       0.85      0.56      0.67        61\n",
      "\n",
      "    accuracy                           0.98      2000\n",
      "   macro avg       0.92      0.78      0.83      2000\n",
      "weighted avg       0.98      0.98      0.98      2000\n",
      "\n",
      "Best Hyperparameters: {'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 200}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature scaling\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Model training\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Model evaluation\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Hyperparameter tuning (optional)\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [None, 10, 20],\n",
    "    'min_samples_split': [2, 5, 10]\n",
    "}\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, n_jobs=-1)\n",
    "grid_search.fit(X_train_scaled, y_train)\n",
    "best_params = grid_search.best_params_\n",
    "print(\"Best Hyperparameters:\", best_params)\n",
    "\n",
    "# Re-train the model with the best hyperparameters\n",
    "best_model = RandomForestClassifier(**best_params)\n",
    "best_model.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Utilize models in predictive analytics to forecast failures and support decision-making processes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop feature names from input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_encoded_without_names = X_encoded.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = best_model.predict(X_encoded_without_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilize model predictions for decision-making"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "maintenance_needed_indices = predictions.nonzero()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maintenance is predicted to be needed for the following instances:\n",
      "- Instance 1\n",
      "- Instance 2\n",
      "- Instance 3\n",
      "- Instance 4\n",
      "- Instance 5\n",
      "- Instance 6\n",
      "- Instance 7\n",
      "- Instance 8\n",
      "- Instance 9\n",
      "- Instance 10\n",
      "- Instance 11\n",
      "- Instance 12\n",
      "- Instance 13\n",
      "- Instance 14\n",
      "- Instance 15\n",
      "- Instance 16\n",
      "- Instance 17\n",
      "- Instance 18\n",
      "- Instance 19\n",
      "- Instance 20\n",
      "- Instance 21\n",
      "- Instance 22\n",
      "- Instance 23\n",
      "- Instance 24\n",
      "- Instance 25\n",
      "- Instance 26\n",
      "- Instance 27\n",
      "- Instance 28\n",
      "- Instance 29\n",
      "- Instance 30\n",
      "- Instance 31\n",
      "- Instance 32\n",
      "- Instance 33\n",
      "- Instance 34\n",
      "- Instance 35\n",
      "- Instance 36\n",
      "- Instance 37\n",
      "- Instance 38\n",
      "- Instance 39\n",
      "- Instance 40\n",
      "- Instance 41\n",
      "- Instance 42\n",
      "- Instance 43\n",
      "- Instance 44\n",
      "- Instance 45\n",
      "- Instance 46\n",
      "- Instance 47\n",
      "- Instance 48\n",
      "- Instance 49\n",
      "- Instance 50\n",
      "- Instance 51\n",
      "- Instance 52\n",
      "- Instance 53\n",
      "- Instance 54\n",
      "- Instance 55\n",
      "- Instance 56\n",
      "- Instance 57\n",
      "- Instance 58\n",
      "- Instance 59\n",
      "- Instance 60\n",
      "- Instance 61\n",
      "- Instance 62\n",
      "- Instance 63\n",
      "- Instance 64\n",
      "- Instance 65\n",
      "- Instance 66\n",
      "- Instance 67\n",
      "- Instance 68\n",
      "- Instance 69\n",
      "- Instance 70\n",
      "- Instance 71\n",
      "- Instance 72\n",
      "- Instance 73\n",
      "- Instance 74\n",
      "- Instance 75\n",
      "- Instance 76\n",
      "- Instance 77\n",
      "- Instance 78\n",
      "- Instance 79\n",
      "- Instance 80\n",
      "- Instance 81\n",
      "- Instance 82\n",
      "- Instance 83\n",
      "- Instance 84\n",
      "- Instance 85\n",
      "- Instance 86\n",
      "- Instance 87\n",
      "- Instance 88\n",
      "- Instance 89\n",
      "- Instance 90\n",
      "- Instance 91\n",
      "- Instance 92\n",
      "- Instance 93\n",
      "- Instance 94\n",
      "- Instance 95\n",
      "- Instance 96\n",
      "- Instance 97\n",
      "- Instance 98\n",
      "- Instance 99\n",
      "- Instance 100\n",
      "- Instance 101\n",
      "- Instance 102\n",
      "- Instance 103\n",
      "- Instance 104\n",
      "- Instance 105\n",
      "- Instance 106\n",
      "- Instance 107\n",
      "- Instance 108\n",
      "- Instance 109\n",
      "- Instance 110\n",
      "- Instance 111\n",
      "- Instance 112\n",
      "- Instance 113\n",
      "- Instance 114\n",
      "- Instance 115\n",
      "- Instance 116\n",
      "- Instance 117\n",
      "- Instance 118\n",
      "- Instance 119\n",
      "- Instance 120\n",
      "- Instance 121\n",
      "- Instance 122\n",
      "- Instance 123\n",
      "- Instance 124\n",
      "- Instance 125\n",
      "- Instance 126\n",
      "- Instance 127\n",
      "- Instance 128\n",
      "- Instance 129\n",
      "- Instance 130\n",
      "- Instance 131\n",
      "- Instance 132\n",
      "- Instance 133\n",
      "- Instance 134\n",
      "- Instance 135\n",
      "- Instance 136\n",
      "- Instance 137\n",
      "- Instance 138\n",
      "- Instance 139\n",
      "- Instance 140\n",
      "- Instance 141\n",
      "- Instance 142\n",
      "- Instance 143\n",
      "- Instance 144\n",
      "- Instance 145\n",
      "- Instance 146\n",
      "- Instance 147\n",
      "- Instance 148\n",
      "- Instance 149\n",
      "- Instance 150\n",
      "- Instance 151\n",
      "- Instance 152\n",
      "- Instance 153\n",
      "- Instance 154\n",
      "- Instance 155\n",
      "- Instance 156\n",
      "- Instance 157\n",
      "- Instance 158\n",
      "- Instance 159\n",
      "- Instance 160\n",
      "- Instance 161\n",
      "- Instance 162\n",
      "- Instance 163\n",
      "- Instance 164\n",
      "- Instance 165\n",
      "- Instance 166\n",
      "- Instance 167\n",
      "- Instance 168\n",
      "- Instance 169\n",
      "- Instance 170\n",
      "- Instance 171\n",
      "- Instance 172\n",
      "- Instance 173\n",
      "- Instance 174\n",
      "- Instance 175\n",
      "- Instance 176\n",
      "- Instance 177\n",
      "- Instance 178\n",
      "- Instance 179\n",
      "- Instance 180\n",
      "- Instance 181\n",
      "- Instance 182\n",
      "- Instance 183\n",
      "- Instance 184\n",
      "- Instance 185\n",
      "- Instance 186\n",
      "- Instance 187\n",
      "- Instance 188\n",
      "- Instance 189\n",
      "- Instance 190\n",
      "- Instance 191\n",
      "- Instance 192\n",
      "- Instance 193\n",
      "- Instance 194\n",
      "- Instance 195\n",
      "- Instance 196\n",
      "- Instance 197\n",
      "- Instance 198\n",
      "- Instance 199\n",
      "- Instance 200\n",
      "- Instance 201\n",
      "- Instance 202\n",
      "- Instance 203\n",
      "- Instance 204\n",
      "- Instance 205\n",
      "- Instance 206\n",
      "- Instance 207\n",
      "- Instance 208\n",
      "- Instance 209\n",
      "- Instance 210\n",
      "- Instance 211\n",
      "- Instance 212\n",
      "- Instance 213\n",
      "- Instance 214\n",
      "- Instance 215\n",
      "- Instance 216\n",
      "- Instance 217\n",
      "- Instance 218\n",
      "- Instance 219\n",
      "- Instance 220\n",
      "- Instance 221\n",
      "- Instance 222\n",
      "- Instance 223\n",
      "- Instance 224\n",
      "- Instance 225\n",
      "- Instance 226\n",
      "- Instance 227\n",
      "- Instance 228\n",
      "- Instance 229\n",
      "- Instance 230\n",
      "- Instance 231\n",
      "- Instance 232\n",
      "- Instance 233\n",
      "- Instance 234\n",
      "- Instance 235\n",
      "- Instance 236\n",
      "- Instance 237\n",
      "- Instance 238\n",
      "- Instance 239\n",
      "- Instance 240\n",
      "- Instance 241\n",
      "- Instance 242\n",
      "- Instance 243\n",
      "- Instance 244\n",
      "- Instance 245\n",
      "- Instance 246\n",
      "- Instance 247\n",
      "- Instance 248\n",
      "- Instance 249\n",
      "- Instance 250\n",
      "- Instance 251\n",
      "- Instance 252\n",
      "- Instance 253\n",
      "- Instance 254\n",
      "- Instance 255\n",
      "- Instance 256\n",
      "- Instance 257\n",
      "- Instance 258\n",
      "- Instance 259\n",
      "- Instance 260\n",
      "- Instance 261\n",
      "- Instance 262\n",
      "- Instance 263\n",
      "- Instance 264\n",
      "- Instance 265\n",
      "- Instance 266\n",
      "- Instance 267\n",
      "- Instance 268\n",
      "- Instance 269\n",
      "- Instance 270\n",
      "- Instance 271\n",
      "- Instance 272\n",
      "- Instance 273\n",
      "- Instance 274\n",
      "- Instance 275\n",
      "- Instance 276\n",
      "- Instance 277\n",
      "- Instance 278\n",
      "- Instance 279\n",
      "- Instance 280\n",
      "- Instance 281\n",
      "- Instance 282\n",
      "- Instance 283\n",
      "- Instance 284\n",
      "- Instance 285\n",
      "- Instance 286\n",
      "- Instance 287\n",
      "- Instance 288\n",
      "- Instance 289\n",
      "- Instance 290\n",
      "- Instance 291\n",
      "- Instance 292\n",
      "- Instance 293\n",
      "- Instance 294\n",
      "- Instance 295\n",
      "- Instance 296\n",
      "- Instance 297\n",
      "- Instance 298\n",
      "- Instance 299\n",
      "- Instance 300\n",
      "- Instance 301\n",
      "- Instance 302\n",
      "- Instance 303\n",
      "- Instance 304\n",
      "- Instance 305\n",
      "- Instance 306\n",
      "- Instance 307\n",
      "- Instance 308\n",
      "- Instance 309\n",
      "- Instance 310\n",
      "- Instance 311\n",
      "- Instance 312\n",
      "- Instance 313\n",
      "- Instance 314\n",
      "- Instance 315\n",
      "- Instance 316\n",
      "- Instance 317\n",
      "- Instance 318\n",
      "- Instance 319\n",
      "- Instance 320\n",
      "- Instance 321\n",
      "- Instance 322\n",
      "- Instance 323\n",
      "- Instance 324\n",
      "- Instance 325\n",
      "- Instance 326\n",
      "- Instance 327\n",
      "- Instance 328\n",
      "- Instance 329\n",
      "- Instance 330\n",
      "- Instance 331\n",
      "- Instance 332\n",
      "- Instance 333\n",
      "- Instance 334\n",
      "- Instance 335\n",
      "- Instance 336\n",
      "- Instance 337\n",
      "- Instance 338\n",
      "- Instance 339\n",
      "- Instance 340\n",
      "- Instance 341\n",
      "- Instance 342\n",
      "- Instance 343\n",
      "- Instance 344\n",
      "- Instance 345\n",
      "- Instance 346\n",
      "- Instance 347\n",
      "- Instance 348\n",
      "- Instance 349\n",
      "- Instance 350\n",
      "- Instance 351\n",
      "- Instance 352\n",
      "- Instance 353\n",
      "- Instance 354\n",
      "- Instance 355\n",
      "- Instance 356\n",
      "- Instance 357\n",
      "- Instance 358\n",
      "- Instance 359\n",
      "- Instance 360\n",
      "- Instance 361\n",
      "- Instance 362\n",
      "- Instance 363\n",
      "- Instance 364\n",
      "- Instance 365\n",
      "- Instance 366\n",
      "- Instance 367\n",
      "- Instance 368\n",
      "- Instance 369\n",
      "- Instance 370\n",
      "- Instance 371\n",
      "- Instance 372\n",
      "- Instance 373\n",
      "- Instance 374\n",
      "- Instance 375\n",
      "- Instance 376\n",
      "- Instance 377\n",
      "- Instance 378\n",
      "- Instance 379\n",
      "- Instance 380\n",
      "- Instance 381\n",
      "- Instance 382\n",
      "- Instance 383\n",
      "- Instance 384\n",
      "- Instance 385\n",
      "- Instance 386\n",
      "- Instance 387\n",
      "- Instance 388\n",
      "- Instance 389\n",
      "- Instance 390\n",
      "- Instance 391\n",
      "- Instance 392\n",
      "- Instance 393\n",
      "- Instance 394\n",
      "- Instance 395\n",
      "- Instance 396\n",
      "- Instance 397\n",
      "- Instance 398\n",
      "- Instance 399\n",
      "- Instance 400\n",
      "- Instance 401\n",
      "- Instance 402\n",
      "- Instance 403\n",
      "- Instance 404\n",
      "- Instance 405\n",
      "- Instance 406\n",
      "- Instance 407\n",
      "- Instance 408\n",
      "- Instance 409\n",
      "- Instance 410\n",
      "- Instance 411\n",
      "- Instance 412\n",
      "- Instance 413\n",
      "- Instance 414\n",
      "- Instance 415\n",
      "- Instance 416\n",
      "- Instance 417\n",
      "- Instance 418\n",
      "- Instance 419\n",
      "- Instance 420\n",
      "- Instance 421\n",
      "- Instance 422\n",
      "- Instance 423\n",
      "- Instance 424\n",
      "- Instance 425\n",
      "- Instance 426\n",
      "- Instance 427\n",
      "- Instance 428\n",
      "- Instance 429\n",
      "- Instance 430\n",
      "- Instance 431\n",
      "- Instance 432\n",
      "- Instance 433\n",
      "- Instance 434\n",
      "- Instance 435\n",
      "- Instance 436\n",
      "- Instance 437\n",
      "- Instance 438\n",
      "- Instance 439\n",
      "- Instance 440\n",
      "- Instance 441\n",
      "- Instance 442\n",
      "- Instance 443\n",
      "- Instance 444\n",
      "- Instance 445\n",
      "- Instance 446\n",
      "- Instance 447\n",
      "- Instance 448\n",
      "- Instance 449\n",
      "- Instance 450\n",
      "- Instance 451\n",
      "- Instance 452\n",
      "- Instance 453\n",
      "- Instance 454\n",
      "- Instance 455\n",
      "- Instance 456\n",
      "- Instance 457\n",
      "- Instance 458\n",
      "- Instance 459\n",
      "- Instance 460\n",
      "- Instance 461\n",
      "- Instance 462\n",
      "- Instance 463\n",
      "- Instance 464\n",
      "- Instance 465\n",
      "- Instance 466\n",
      "- Instance 467\n",
      "- Instance 468\n",
      "- Instance 469\n",
      "- Instance 470\n",
      "- Instance 471\n",
      "- Instance 472\n",
      "- Instance 473\n",
      "- Instance 474\n",
      "- Instance 475\n",
      "- Instance 476\n",
      "- Instance 477\n",
      "- Instance 478\n",
      "- Instance 479\n",
      "- Instance 480\n",
      "- Instance 481\n",
      "- Instance 482\n",
      "- Instance 483\n",
      "- Instance 484\n",
      "- Instance 485\n",
      "- Instance 486\n",
      "- Instance 487\n",
      "- Instance 488\n",
      "- Instance 489\n",
      "- Instance 490\n",
      "- Instance 491\n",
      "- Instance 492\n",
      "- Instance 493\n",
      "- Instance 494\n",
      "- Instance 495\n",
      "- Instance 496\n",
      "- Instance 497\n",
      "- Instance 498\n",
      "- Instance 499\n",
      "- Instance 500\n",
      "- Instance 501\n",
      "- Instance 502\n",
      "- Instance 503\n",
      "- Instance 504\n",
      "- Instance 505\n",
      "- Instance 506\n",
      "- Instance 507\n",
      "- Instance 508\n",
      "- Instance 509\n",
      "- Instance 510\n",
      "- Instance 511\n",
      "- Instance 512\n",
      "- Instance 513\n",
      "- Instance 514\n",
      "- Instance 515\n",
      "- Instance 516\n",
      "- Instance 517\n",
      "- Instance 518\n",
      "- Instance 519\n",
      "- Instance 520\n",
      "- Instance 521\n",
      "- Instance 522\n",
      "- Instance 523\n",
      "- Instance 524\n",
      "- Instance 525\n",
      "- Instance 526\n",
      "- Instance 527\n",
      "- Instance 528\n",
      "- Instance 529\n",
      "- Instance 530\n",
      "- Instance 531\n",
      "- Instance 532\n",
      "- Instance 533\n",
      "- Instance 534\n",
      "- Instance 535\n",
      "- Instance 536\n",
      "- Instance 537\n",
      "- Instance 538\n",
      "- Instance 539\n",
      "- Instance 540\n",
      "- Instance 541\n",
      "- Instance 542\n",
      "- Instance 543\n",
      "- Instance 544\n",
      "- Instance 545\n",
      "- Instance 546\n",
      "- Instance 547\n",
      "- Instance 548\n",
      "- Instance 549\n",
      "- Instance 550\n",
      "- Instance 551\n",
      "- Instance 552\n",
      "- Instance 553\n",
      "- Instance 554\n",
      "- Instance 555\n",
      "- Instance 556\n",
      "- Instance 557\n",
      "- Instance 558\n",
      "- Instance 559\n",
      "- Instance 560\n",
      "- Instance 561\n",
      "- Instance 562\n",
      "- Instance 563\n",
      "- Instance 564\n",
      "- Instance 565\n",
      "- Instance 566\n",
      "- Instance 567\n",
      "- Instance 568\n",
      "- Instance 569\n",
      "- Instance 570\n",
      "- Instance 571\n",
      "- Instance 572\n",
      "- Instance 573\n",
      "- Instance 574\n",
      "- Instance 575\n",
      "- Instance 576\n",
      "- Instance 577\n",
      "- Instance 578\n",
      "- Instance 579\n",
      "- Instance 580\n",
      "- Instance 581\n",
      "- Instance 582\n",
      "- Instance 583\n",
      "- Instance 584\n",
      "- Instance 585\n",
      "- Instance 586\n",
      "- Instance 587\n",
      "- Instance 588\n",
      "- Instance 589\n",
      "- Instance 590\n",
      "- Instance 591\n",
      "- Instance 592\n",
      "- Instance 593\n",
      "- Instance 594\n",
      "- Instance 595\n",
      "- Instance 596\n",
      "- Instance 597\n",
      "- Instance 598\n",
      "- Instance 599\n",
      "- Instance 600\n",
      "- Instance 601\n",
      "- Instance 602\n",
      "- Instance 603\n",
      "- Instance 604\n",
      "- Instance 605\n",
      "- Instance 606\n",
      "- Instance 607\n",
      "- Instance 608\n",
      "- Instance 609\n",
      "- Instance 610\n",
      "- Instance 611\n",
      "- Instance 612\n",
      "- Instance 613\n",
      "- Instance 614\n",
      "- Instance 615\n",
      "- Instance 616\n",
      "- Instance 617\n",
      "- Instance 618\n",
      "- Instance 619\n",
      "- Instance 620\n",
      "- Instance 621\n",
      "- Instance 622\n",
      "- Instance 623\n",
      "- Instance 624\n",
      "- Instance 625\n",
      "- Instance 626\n",
      "- Instance 627\n",
      "- Instance 628\n",
      "- Instance 629\n",
      "- Instance 630\n",
      "- Instance 631\n",
      "- Instance 632\n",
      "- Instance 633\n",
      "- Instance 634\n",
      "- Instance 635\n",
      "- Instance 636\n",
      "- Instance 637\n",
      "- Instance 638\n",
      "- Instance 639\n",
      "- Instance 640\n",
      "- Instance 641\n",
      "- Instance 642\n",
      "- Instance 643\n",
      "- Instance 644\n",
      "- Instance 645\n",
      "- Instance 646\n",
      "- Instance 647\n",
      "- Instance 648\n",
      "- Instance 649\n",
      "- Instance 650\n",
      "- Instance 651\n",
      "- Instance 652\n",
      "- Instance 653\n",
      "- Instance 654\n",
      "- Instance 655\n",
      "- Instance 656\n",
      "- Instance 657\n",
      "- Instance 658\n",
      "- Instance 659\n",
      "- Instance 660\n",
      "- Instance 661\n",
      "- Instance 662\n",
      "- Instance 663\n",
      "- Instance 664\n",
      "- Instance 665\n",
      "- Instance 666\n",
      "- Instance 667\n",
      "- Instance 668\n",
      "- Instance 669\n",
      "- Instance 670\n",
      "- Instance 671\n",
      "- Instance 672\n",
      "- Instance 673\n",
      "- Instance 674\n",
      "- Instance 675\n",
      "- Instance 676\n",
      "- Instance 677\n",
      "- Instance 678\n",
      "- Instance 679\n",
      "- Instance 680\n",
      "- Instance 681\n",
      "- Instance 682\n",
      "- Instance 683\n",
      "- Instance 684\n",
      "- Instance 685\n",
      "- Instance 686\n",
      "- Instance 687\n",
      "- Instance 688\n",
      "- Instance 689\n",
      "- Instance 690\n",
      "- Instance 691\n",
      "- Instance 692\n",
      "- Instance 693\n",
      "- Instance 694\n",
      "- Instance 695\n",
      "- Instance 696\n",
      "- Instance 697\n",
      "- Instance 698\n",
      "- Instance 699\n",
      "- Instance 700\n",
      "- Instance 701\n",
      "- Instance 702\n",
      "- Instance 703\n",
      "- Instance 704\n",
      "- Instance 705\n",
      "- Instance 706\n",
      "- Instance 707\n",
      "- Instance 708\n",
      "- Instance 709\n",
      "- Instance 710\n",
      "- Instance 711\n",
      "- Instance 712\n",
      "- Instance 713\n",
      "- Instance 714\n",
      "- Instance 715\n",
      "- Instance 716\n",
      "- Instance 717\n",
      "- Instance 718\n",
      "- Instance 719\n",
      "- Instance 720\n",
      "- Instance 721\n",
      "- Instance 722\n",
      "- Instance 723\n",
      "- Instance 724\n",
      "- Instance 725\n",
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      "- Instance 727\n",
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      "- Instance 729\n",
      "- Instance 730\n",
      "- Instance 731\n",
      "- Instance 732\n",
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      "- Instance 737\n",
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      "- Instance 739\n",
      "- Instance 740\n",
      "- Instance 741\n",
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      "- Instance 744\n",
      "- Instance 745\n",
      "- Instance 746\n",
      "- Instance 747\n",
      "- Instance 748\n",
      "- Instance 749\n",
      "- Instance 750\n",
      "- Instance 751\n",
      "- Instance 752\n",
      "- Instance 753\n",
      "- Instance 754\n",
      "- Instance 755\n",
      "- Instance 756\n",
      "- Instance 757\n",
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      "- Instance 761\n",
      "- Instance 762\n",
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      "- Instance 768\n",
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      "- Instance 770\n",
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      "- Instance 777\n",
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      "- Instance 788\n",
      "- Instance 789\n",
      "- Instance 790\n",
      "- Instance 791\n",
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      "- Instance 800\n",
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      "- Instance 1460\n",
      "- Instance 1461\n",
      "- Instance 1462\n",
      "- Instance 1463\n",
      "- Instance 1464\n",
      "- Instance 1465\n",
      "- Instance 1466\n",
      "- Instance 1467\n",
      "- Instance 1468\n",
      "- Instance 1469\n",
      "- Instance 1470\n",
      "- Instance 1471\n",
      "- Instance 1472\n",
      "- Instance 1473\n",
      "- Instance 1474\n",
      "- Instance 1475\n",
      "- Instance 1476\n",
      "- Instance 1477\n",
      "- Instance 1478\n",
      "- Instance 1479\n",
      "- Instance 1480\n",
      "- Instance 1481\n",
      "- Instance 1482\n",
      "- Instance 1483\n",
      "- Instance 1484\n",
      "- Instance 1485\n",
      "- Instance 1486\n",
      "- Instance 1487\n",
      "- Instance 1488\n",
      "- Instance 1489\n",
      "- Instance 1490\n",
      "- Instance 1491\n",
      "- Instance 1492\n",
      "- Instance 1493\n",
      "- Instance 1494\n",
      "- Instance 1495\n",
      "- Instance 1496\n",
      "- Instance 1497\n",
      "- Instance 1498\n",
      "- Instance 1499\n",
      "- Instance 1500\n",
      "- Instance 1501\n",
      "- Instance 1502\n",
      "- Instance 1503\n",
      "- Instance 1504\n",
      "- Instance 1505\n",
      "- Instance 1506\n",
      "- Instance 1507\n",
      "- Instance 1508\n",
      "- Instance 1509\n",
      "- Instance 1510\n",
      "- Instance 1511\n",
      "- Instance 1512\n",
      "- Instance 1513\n",
      "- Instance 1514\n",
      "- Instance 1515\n",
      "- Instance 1516\n",
      "- Instance 1517\n",
      "- Instance 1518\n",
      "- Instance 1519\n",
      "- Instance 1520\n",
      "- Instance 1521\n",
      "- Instance 1522\n",
      "- Instance 1523\n",
      "- Instance 1524\n",
      "- Instance 1525\n",
      "- Instance 1526\n",
      "- Instance 1527\n",
      "- Instance 1528\n",
      "- Instance 1529\n",
      "- Instance 1530\n",
      "- Instance 1531\n",
      "- Instance 1532\n",
      "- Instance 1533\n",
      "- Instance 1534\n",
      "- Instance 1535\n",
      "- Instance 1536\n",
      "- Instance 1537\n",
      "- Instance 1538\n",
      "- Instance 1539\n",
      "- Instance 1540\n",
      "- Instance 1541\n",
      "- Instance 1542\n",
      "- Instance 1543\n",
      "- Instance 1544\n",
      "- Instance 1545\n",
      "- Instance 1546\n",
      "- Instance 1547\n",
      "- Instance 1548\n",
      "- Instance 1549\n",
      "- Instance 1550\n",
      "- Instance 1551\n",
      "- Instance 1552\n",
      "- Instance 1553\n",
      "- Instance 1554\n",
      "- Instance 1555\n",
      "- Instance 1556\n",
      "- Instance 1557\n",
      "- Instance 1558\n",
      "- Instance 1559\n",
      "- Instance 1560\n",
      "- Instance 1561\n",
      "- Instance 1562\n",
      "- Instance 1563\n",
      "- Instance 1564\n",
      "- Instance 1565\n",
      "- Instance 1566\n",
      "- Instance 1567\n",
      "- Instance 1568\n",
      "- Instance 1569\n",
      "- Instance 1570\n",
      "- Instance 1571\n",
      "- Instance 1572\n",
      "- Instance 1573\n",
      "- Instance 1574\n",
      "- Instance 1575\n",
      "- Instance 1576\n",
      "- Instance 1577\n",
      "- Instance 1578\n",
      "- Instance 1579\n",
      "- Instance 1580\n",
      "- Instance 1581\n",
      "- Instance 1582\n",
      "- Instance 1583\n",
      "- Instance 1584\n",
      "- Instance 1585\n",
      "- Instance 1586\n",
      "- Instance 1587\n",
      "- Instance 1588\n",
      "- Instance 1589\n",
      "- Instance 1590\n",
      "- Instance 1591\n",
      "- Instance 1592\n",
      "- Instance 1593\n",
      "- Instance 1594\n",
      "- Instance 1595\n",
      "- Instance 1596\n",
      "- Instance 1597\n",
      "- Instance 1598\n",
      "- Instance 1599\n",
      "- Instance 1600\n",
      "- Instance 1601\n",
      "- Instance 1602\n",
      "- Instance 1603\n",
      "- Instance 1604\n",
      "- Instance 1605\n",
      "- Instance 1606\n",
      "- Instance 1607\n",
      "- Instance 1608\n",
      "- Instance 1609\n",
      "- Instance 1610\n",
      "- Instance 1611\n",
      "- Instance 1612\n",
      "- Instance 1613\n",
      "- Instance 1614\n",
      "- Instance 1615\n",
      "- Instance 1616\n",
      "- Instance 1617\n",
      "- Instance 1618\n",
      "- Instance 1619\n",
      "- Instance 1620\n",
      "- Instance 1621\n",
      "- Instance 1622\n",
      "- Instance 1623\n",
      "- Instance 1624\n",
      "- Instance 1625\n",
      "- Instance 1626\n",
      "- Instance 1627\n",
      "- Instance 1628\n",
      "- Instance 1629\n",
      "- Instance 1630\n",
      "- Instance 1631\n",
      "- Instance 1632\n",
      "- Instance 1633\n",
      "- Instance 1634\n",
      "- Instance 1635\n",
      "- Instance 1636\n",
      "- Instance 1637\n",
      "- Instance 1638\n",
      "- Instance 1639\n",
      "- Instance 1640\n",
      "- Instance 1641\n",
      "- Instance 1642\n",
      "- Instance 1643\n",
      "- Instance 1644\n",
      "- Instance 1645\n",
      "- Instance 1646\n",
      "- Instance 1647\n",
      "- Instance 1648\n",
      "- Instance 1649\n",
      "- Instance 1650\n",
      "- Instance 1651\n",
      "- Instance 1652\n",
      "- Instance 1653\n",
      "- Instance 1654\n",
      "- Instance 1655\n",
      "- Instance 1656\n",
      "- Instance 1657\n",
      "- Instance 1658\n",
      "- Instance 1659\n",
      "- Instance 1660\n",
      "- Instance 1661\n",
      "- Instance 1662\n",
      "- Instance 1663\n",
      "- Instance 1664\n",
      "- Instance 1665\n",
      "- Instance 1666\n",
      "- Instance 1667\n",
      "- Instance 1668\n",
      "- Instance 1669\n",
      "- Instance 1670\n",
      "- Instance 1671\n",
      "- Instance 1672\n",
      "- Instance 1673\n",
      "- Instance 1674\n",
      "- Instance 1675\n",
      "- Instance 1676\n",
      "- Instance 1677\n",
      "- Instance 1678\n",
      "- Instance 1679\n",
      "- Instance 1680\n",
      "- Instance 1681\n",
      "- Instance 1682\n",
      "- Instance 1683\n",
      "- Instance 1684\n",
      "- Instance 1685\n",
      "- Instance 1686\n",
      "- Instance 1687\n",
      "- Instance 1688\n",
      "- Instance 1689\n",
      "- Instance 1690\n",
      "- Instance 1691\n",
      "- Instance 1692\n",
      "- Instance 1693\n",
      "- Instance 1694\n",
      "- Instance 1695\n",
      "- Instance 1696\n",
      "- Instance 1697\n",
      "- Instance 1698\n",
      "- Instance 1699\n",
      "- Instance 1700\n",
      "- Instance 1701\n",
      "- Instance 1702\n",
      "- Instance 1703\n",
      "- Instance 1704\n",
      "- Instance 1705\n",
      "- Instance 1706\n",
      "- Instance 1707\n",
      "- Instance 1708\n",
      "- Instance 1709\n",
      "- Instance 1710\n",
      "- Instance 1711\n",
      "- Instance 1712\n",
      "- Instance 1713\n",
      "- Instance 1714\n",
      "- Instance 1715\n",
      "- Instance 1716\n",
      "- Instance 1717\n",
      "- Instance 1718\n",
      "- Instance 1719\n",
      "- Instance 1720\n",
      "- Instance 1721\n",
      "- Instance 1722\n",
      "- Instance 1723\n",
      "- Instance 1724\n",
      "- Instance 1725\n",
      "- Instance 1726\n",
      "- Instance 1727\n",
      "- Instance 1728\n",
      "- Instance 1729\n",
      "- Instance 1730\n",
      "- Instance 1731\n",
      "- Instance 1732\n",
      "- Instance 1733\n",
      "- Instance 1734\n",
      "- Instance 1735\n",
      "- Instance 1736\n",
      "- Instance 1737\n",
      "- Instance 1738\n",
      "- Instance 1739\n",
      "- Instance 1740\n",
      "- Instance 1741\n",
      "- Instance 1742\n",
      "- Instance 1743\n",
      "- Instance 1744\n",
      "- Instance 1745\n",
      "- Instance 1746\n",
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      "- Instance 1748\n",
      "- Instance 1749\n",
      "- Instance 1750\n",
      "- Instance 1751\n",
      "- Instance 1752\n",
      "- Instance 1753\n",
      "- Instance 1754\n",
      "- Instance 1755\n",
      "- Instance 1756\n",
      "- Instance 1757\n",
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      "- Instance 1760\n",
      "- Instance 1761\n",
      "- Instance 1762\n",
      "- Instance 1763\n",
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      "- Instance 1772\n",
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      "- Instance 1776\n",
      "- Instance 1777\n",
      "- Instance 1778\n",
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      "- Instance 1780\n",
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      "- Instance 1782\n",
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      "- Instance 1786\n",
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      "- Instance 1790\n",
      "- Instance 1791\n",
      "- Instance 1792\n",
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      "- Instance 1796\n",
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      "- Instance 1798\n",
      "- Instance 1799\n",
      "- Instance 1800\n",
      "- Instance 1801\n",
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      "- Instance 1830\n",
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      "- Instance 1840\n",
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      "- Instance 1850\n",
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      "- Instance 1860\n",
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      "- Instance 1876\n",
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      "- Instance 1880\n",
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      "- Instance 1921\n",
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      "- Instance 1941\n",
      "- Instance 1942\n",
      "- Instance 1943\n",
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      "- Instance 1945\n",
      "- Instance 1946\n",
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      "- Instance 1951\n",
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      "- Instance 1975\n",
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      "- Instance 1980\n",
      "- Instance 1981\n",
      "- Instance 1982\n",
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      "- Instance 1984\n",
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      "- Instance 1986\n",
      "- Instance 1987\n",
      "- Instance 1988\n",
      "- Instance 1989\n",
      "- Instance 1990\n",
      "- Instance 1991\n",
      "- Instance 1992\n",
      "- Instance 1993\n",
      "- Instance 1994\n",
      "- Instance 1995\n",
      "- Instance 1996\n",
      "- Instance 1997\n",
      "- Instance 1998\n",
      "- Instance 1999\n",
      "- Instance 2000\n",
      "- Instance 2001\n",
      "- Instance 2002\n",
      "- Instance 2003\n",
      "- Instance 2004\n",
      "- Instance 2005\n",
      "- Instance 2006\n",
      "- Instance 2007\n",
      "- Instance 2008\n",
      "- Instance 2009\n",
      "- Instance 2010\n",
      "- Instance 2011\n",
      "- Instance 2012\n",
      "- Instance 2013\n",
      "- Instance 2014\n",
      "- Instance 2015\n",
      "- Instance 2016\n",
      "- Instance 2017\n",
      "- Instance 2018\n",
      "- Instance 2019\n",
      "- Instance 2020\n",
      "- Instance 2021\n",
      "- Instance 2022\n",
      "- Instance 2023\n",
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      "- Instance 2030\n",
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      "- Instance 2038\n",
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      "- Instance 2040\n",
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      "- Instance 2042\n",
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      "- Instance 2044\n",
      "- Instance 2045\n",
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      "- Instance 2048\n",
      "- Instance 2049\n",
      "- Instance 2050\n",
      "- Instance 2051\n",
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      "- Instance 2064\n",
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      "- Instance 2070\n",
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      "- Instance 2072\n",
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      "- Instance 2081\n",
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      "- Instance 2089\n",
      "- Instance 2090\n",
      "- Instance 2091\n",
      "- Instance 2092\n",
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      "- Instance 2098\n",
      "- Instance 2099\n",
      "- Instance 2100\n",
      "- Instance 2101\n",
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      "- Instance 2110\n",
      "- Instance 2111\n",
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      "- Instance 2117\n",
      "- Instance 2118\n",
      "- Instance 2119\n",
      "- Instance 2120\n",
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      "- Instance 2122\n",
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      "- Instance 2141\n",
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      "- Instance 2144\n",
      "- Instance 2145\n",
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      "- Instance 2153\n",
      "- Instance 2154\n",
      "- Instance 2155\n",
      "- Instance 2156\n",
      "- Instance 2157\n",
      "- Instance 2158\n",
      "- Instance 2159\n",
      "- Instance 2160\n",
      "- Instance 2161\n",
      "- Instance 2162\n",
      "- Instance 2163\n",
      "- Instance 2164\n",
      "- Instance 2165\n",
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      "- Instance 2167\n",
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      "- Instance 2169\n",
      "- Instance 2170\n",
      "- Instance 2171\n",
      "- Instance 2172\n",
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      "- Instance 2189\n",
      "- Instance 2190\n",
      "- Instance 2191\n",
      "- Instance 2192\n",
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      "- Instance 2210\n",
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      "- Instance 2217\n",
      "- Instance 2218\n",
      "- Instance 2219\n",
      "- Instance 2220\n",
      "- Instance 2221\n",
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      "- Instance 2241\n",
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      "- Instance 2959\n",
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      "- Instance 2961\n",
      "- Instance 2962\n",
      "- Instance 2963\n",
      "- Instance 2964\n",
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      "- Instance 2986\n",
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      "- Instance 2990\n",
      "- Instance 2991\n",
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      "- Instance 2993\n",
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      "- Instance 2995\n",
      "- Instance 2996\n",
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      "- Instance 2998\n",
      "- Instance 2999\n",
      "- Instance 3000\n",
      "- Instance 3001\n",
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      "- Instance 3051\n",
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      "- Instance 3701\n",
      "- Instance 3702\n",
      "- Instance 3703\n",
      "- Instance 3704\n",
      "- Instance 3705\n",
      "- Instance 3706\n",
      "- Instance 3707\n",
      "- Instance 3708\n",
      "- Instance 3709\n",
      "- Instance 3710\n",
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      "- Instance 3751\n",
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      "- Instance 3753\n",
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      "- Instance 3755\n",
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      "- Instance 3760\n",
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      "- Instance 3774\n",
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      "- Instance 4445\n",
      "- Instance 4446\n",
      "- Instance 4447\n",
      "- Instance 4448\n",
      "- Instance 4449\n",
      "- Instance 4450\n",
      "- Instance 4451\n",
      "- Instance 4452\n",
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      "- Instance 4459\n",
      "- Instance 4460\n",
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      "- Instance 4471\n",
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      "- Instance 4487\n",
      "- Instance 4488\n",
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      "- Instance 4490\n",
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      "- Instance 5187\n",
      "- Instance 5188\n",
      "- Instance 5189\n",
      "- Instance 5190\n",
      "- Instance 5191\n",
      "- Instance 5192\n",
      "- Instance 5193\n",
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      "- Instance 5927\n",
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      "- Instance 5929\n",
      "- Instance 5930\n",
      "- Instance 5931\n",
      "- Instance 5932\n",
      "- Instance 5933\n",
      "- Instance 5934\n",
      "- Instance 5935\n",
      "- Instance 5936\n",
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      "- Instance 5940\n",
      "- Instance 5941\n",
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      "- Instance 5944\n",
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      "- Instance 5947\n",
      "- Instance 5948\n",
      "- Instance 5949\n",
      "- Instance 5950\n",
      "- Instance 5951\n",
      "- Instance 5952\n",
      "- Instance 5953\n",
      "- Instance 5954\n",
      "- Instance 5955\n",
      "- Instance 5956\n",
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      "- Instance 5959\n",
      "- Instance 5960\n",
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      "- Instance 5962\n",
      "- Instance 5963\n",
      "- Instance 5964\n",
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      "- Instance 5966\n",
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      "- Instance 5970\n",
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      "- Instance 5974\n",
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      "- Instance 5976\n",
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      "- Instance 5979\n",
      "- Instance 5980\n",
      "- Instance 5981\n",
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      "- Instance 5984\n",
      "- Instance 5985\n",
      "- Instance 5986\n",
      "- Instance 5987\n",
      "- Instance 5988\n",
      "- Instance 5989\n",
      "- Instance 5990\n",
      "- Instance 5991\n",
      "- Instance 5992\n",
      "- Instance 5993\n",
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      "- Instance 5995\n",
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      "- Instance 5999\n",
      "- Instance 6000\n",
      "- Instance 6001\n",
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      "- Instance 6669\n",
      "- Instance 6670\n",
      "- Instance 6671\n",
      "- Instance 6672\n",
      "- Instance 6673\n",
      "- Instance 6674\n",
      "- Instance 6675\n",
      "- Instance 6676\n",
      "- Instance 6677\n",
      "- Instance 6678\n",
      "- Instance 6679\n",
      "- Instance 6680\n",
      "- Instance 6681\n",
      "- Instance 6682\n",
      "- Instance 6683\n",
      "- Instance 6684\n",
      "- Instance 6685\n",
      "- Instance 6686\n",
      "- Instance 6687\n",
      "- Instance 6688\n",
      "- Instance 6689\n",
      "- Instance 6690\n",
      "- Instance 6691\n",
      "- Instance 6692\n",
      "- Instance 6693\n",
      "- Instance 6694\n",
      "- Instance 6695\n",
      "- Instance 6696\n",
      "- Instance 6697\n",
      "- Instance 6698\n",
      "- Instance 6699\n",
      "- Instance 6700\n",
      "- Instance 6701\n",
      "- Instance 6702\n",
      "- Instance 6703\n",
      "- Instance 6704\n",
      "- Instance 6705\n",
      "- Instance 6706\n",
      "- Instance 6707\n",
      "- Instance 6708\n",
      "- Instance 6709\n",
      "- Instance 6710\n",
      "- Instance 6711\n",
      "- Instance 6712\n",
      "- Instance 6713\n",
      "- Instance 6714\n",
      "- Instance 6715\n",
      "- Instance 6716\n",
      "- Instance 6717\n",
      "- Instance 6718\n",
      "- Instance 6719\n",
      "- Instance 6720\n",
      "- Instance 6721\n",
      "- Instance 6722\n",
      "- Instance 6723\n",
      "- Instance 6724\n",
      "- Instance 6725\n",
      "- Instance 6726\n",
      "- Instance 6727\n",
      "- Instance 6728\n",
      "- Instance 6729\n",
      "- Instance 6730\n",
      "- Instance 6731\n",
      "- Instance 6732\n",
      "- Instance 6733\n",
      "- Instance 6734\n",
      "- Instance 6735\n",
      "- Instance 6736\n",
      "- Instance 6737\n",
      "- Instance 6738\n",
      "- Instance 6739\n",
      "- Instance 6740\n",
      "- Instance 6741\n",
      "- Instance 6742\n",
      "- Instance 6743\n",
      "- Instance 6744\n",
      "- Instance 6745\n",
      "- Instance 6746\n",
      "- Instance 6747\n",
      "- Instance 6748\n",
      "- Instance 6749\n",
      "- Instance 6750\n",
      "- Instance 6751\n",
      "- Instance 6752\n",
      "- Instance 6753\n",
      "- Instance 6754\n",
      "- Instance 6755\n",
      "- Instance 6756\n",
      "- Instance 6757\n",
      "- Instance 6758\n",
      "- Instance 6759\n",
      "- Instance 6760\n",
      "- Instance 6761\n",
      "- Instance 6762\n",
      "- Instance 6763\n",
      "- Instance 6764\n",
      "- Instance 6765\n",
      "- Instance 6766\n",
      "- Instance 6767\n",
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      "- Instance 6769\n",
      "- Instance 6770\n",
      "- Instance 6771\n",
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      "- Instance 6773\n",
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      "- Instance 6775\n",
      "- Instance 6776\n",
      "- Instance 6777\n",
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      "- Instance 6779\n",
      "- Instance 6780\n",
      "- Instance 6781\n",
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      "- Instance 6783\n",
      "- Instance 6784\n",
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      "- Instance 6788\n",
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      "- Instance 6790\n",
      "- Instance 6791\n",
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      "- Instance 6793\n",
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      "- Instance 6795\n",
      "- Instance 6796\n",
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      "- Instance 6800\n",
      "- Instance 6801\n",
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      "- Instance 7411\n",
      "- Instance 7412\n",
      "- Instance 7413\n",
      "- Instance 7414\n",
      "- Instance 7415\n",
      "- Instance 7416\n",
      "- Instance 7417\n",
      "- Instance 7418\n",
      "- Instance 7419\n",
      "- Instance 7420\n",
      "- Instance 7421\n",
      "- Instance 7422\n",
      "- Instance 7423\n",
      "- Instance 7424\n",
      "- Instance 7425\n",
      "- Instance 7426\n",
      "- Instance 7427\n",
      "- Instance 7428\n",
      "- Instance 7429\n",
      "- Instance 7430\n",
      "- Instance 7431\n",
      "- Instance 7432\n",
      "- Instance 7433\n",
      "- Instance 7434\n",
      "- Instance 7435\n",
      "- Instance 7436\n",
      "- Instance 7437\n",
      "- Instance 7438\n",
      "- Instance 7439\n",
      "- Instance 7440\n",
      "- Instance 7441\n",
      "- Instance 7442\n",
      "- Instance 7443\n",
      "- Instance 7444\n",
      "- Instance 7445\n",
      "- Instance 7446\n",
      "- Instance 7447\n",
      "- Instance 7448\n",
      "- Instance 7449\n",
      "- Instance 7450\n",
      "- Instance 7451\n",
      "- Instance 7452\n",
      "- Instance 7453\n",
      "- Instance 7454\n",
      "- Instance 7455\n",
      "- Instance 7456\n",
      "- Instance 7457\n",
      "- Instance 7458\n",
      "- Instance 7459\n",
      "- Instance 7460\n",
      "- Instance 7461\n",
      "- Instance 7462\n",
      "- Instance 7463\n",
      "- Instance 7464\n",
      "- Instance 7465\n",
      "- Instance 7466\n",
      "- Instance 7467\n",
      "- Instance 7468\n",
      "- Instance 7469\n",
      "- Instance 7470\n",
      "- Instance 7471\n",
      "- Instance 7472\n",
      "- Instance 7473\n",
      "- Instance 7474\n",
      "- Instance 7475\n",
      "- Instance 7476\n",
      "- Instance 7477\n",
      "- Instance 7478\n",
      "- Instance 7479\n",
      "- Instance 7480\n",
      "- Instance 7481\n",
      "- Instance 7482\n",
      "- Instance 7483\n",
      "- Instance 7484\n",
      "- Instance 7485\n",
      "- Instance 7486\n",
      "- Instance 7487\n",
      "- Instance 7488\n",
      "- Instance 7489\n",
      "- Instance 7490\n",
      "- Instance 7491\n",
      "- Instance 7492\n",
      "- Instance 7493\n",
      "- Instance 7494\n",
      "- Instance 7495\n",
      "- Instance 7496\n",
      "- Instance 7497\n",
      "- Instance 7498\n",
      "- Instance 7499\n",
      "- Instance 7500\n",
      "- Instance 7501\n",
      "- Instance 7502\n",
      "- Instance 7503\n",
      "- Instance 7504\n",
      "- Instance 7505\n",
      "- Instance 7506\n",
      "- Instance 7507\n",
      "- Instance 7508\n",
      "- Instance 7509\n",
      "- Instance 7510\n",
      "- Instance 7511\n",
      "- Instance 7512\n",
      "- Instance 7513\n",
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      "- Instance 7515\n",
      "- Instance 7516\n",
      "- Instance 7517\n",
      "- Instance 7518\n",
      "- Instance 7519\n",
      "- Instance 7520\n",
      "- Instance 7521\n",
      "- Instance 7522\n",
      "- Instance 7523\n",
      "- Instance 7524\n",
      "- Instance 7525\n",
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      "- Instance 7527\n",
      "- Instance 7528\n",
      "- Instance 7529\n",
      "- Instance 7530\n",
      "- Instance 7531\n",
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      "- Instance 7534\n",
      "- Instance 7535\n",
      "- Instance 7536\n",
      "- Instance 7537\n",
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      "- Instance 7539\n",
      "- Instance 7540\n",
      "- Instance 7541\n",
      "- Instance 7542\n",
      "- Instance 7543\n",
      "- Instance 7544\n",
      "- Instance 7545\n",
      "- Instance 7546\n",
      "- Instance 7547\n",
      "- Instance 7548\n",
      "- Instance 7549\n",
      "- Instance 7550\n",
      "- Instance 7551\n",
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      "- Instance 7555\n",
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      "- Instance 7560\n",
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      "- Instance 7570\n",
      "- Instance 7571\n",
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      "- Instance 7581\n",
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      "- Instance 7585\n",
      "- Instance 7586\n",
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      "- Instance 7589\n",
      "- Instance 7590\n",
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      "- Instance 8155\n",
      "- Instance 8156\n",
      "- Instance 8157\n",
      "- Instance 8158\n",
      "- Instance 8159\n",
      "- Instance 8160\n",
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      "- Instance 8189\n",
      "- Instance 8190\n",
      "- Instance 8191\n",
      "- Instance 8192\n",
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      "- Instance 8895\n",
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      "- Instance 9637\n",
      "- Instance 9638\n",
      "- Instance 9639\n",
      "- Instance 9640\n",
      "- Instance 9641\n",
      "- Instance 9642\n",
      "- Instance 9643\n",
      "- Instance 9644\n",
      "- Instance 9645\n",
      "- Instance 9646\n",
      "- Instance 9647\n",
      "- Instance 9648\n",
      "- Instance 9649\n",
      "- Instance 9650\n",
      "- Instance 9651\n",
      "- Instance 9652\n",
      "- Instance 9653\n",
      "- Instance 9654\n",
      "- Instance 9655\n",
      "- Instance 9656\n",
      "- Instance 9657\n",
      "- Instance 9658\n",
      "- Instance 9659\n",
      "- Instance 9660\n",
      "- Instance 9661\n",
      "- Instance 9662\n",
      "- Instance 9663\n",
      "- Instance 9664\n",
      "- Instance 9665\n",
      "- Instance 9666\n",
      "- Instance 9667\n",
      "- Instance 9668\n",
      "- Instance 9669\n",
      "- Instance 9670\n",
      "- Instance 9671\n",
      "- Instance 9672\n",
      "- Instance 9673\n",
      "- Instance 9674\n",
      "- Instance 9675\n",
      "- Instance 9676\n",
      "- Instance 9677\n",
      "- Instance 9678\n",
      "- Instance 9679\n",
      "- Instance 9680\n",
      "- Instance 9681\n",
      "- Instance 9682\n",
      "- Instance 9683\n",
      "- Instance 9684\n",
      "- Instance 9685\n",
      "- Instance 9686\n",
      "- Instance 9687\n",
      "- Instance 9688\n",
      "- Instance 9689\n",
      "- Instance 9690\n",
      "- Instance 9691\n",
      "- Instance 9692\n",
      "- Instance 9693\n",
      "- Instance 9694\n",
      "- Instance 9695\n",
      "- Instance 9696\n",
      "- Instance 9697\n",
      "- Instance 9698\n",
      "- Instance 9699\n",
      "- Instance 9700\n",
      "- Instance 9701\n",
      "- Instance 9702\n",
      "- Instance 9703\n",
      "- Instance 9704\n",
      "- Instance 9705\n",
      "- Instance 9706\n",
      "- Instance 9707\n",
      "- Instance 9708\n",
      "- Instance 9709\n",
      "- Instance 9710\n",
      "- Instance 9711\n",
      "- Instance 9712\n",
      "- Instance 9713\n",
      "- Instance 9714\n",
      "- Instance 9715\n",
      "- Instance 9716\n",
      "- Instance 9717\n",
      "- Instance 9718\n",
      "- Instance 9719\n",
      "- Instance 9720\n",
      "- Instance 9721\n",
      "- Instance 9722\n",
      "- Instance 9723\n",
      "- Instance 9724\n",
      "- Instance 9725\n",
      "- Instance 9726\n",
      "- Instance 9727\n",
      "- Instance 9728\n",
      "- Instance 9729\n",
      "- Instance 9730\n",
      "- Instance 9731\n",
      "- Instance 9732\n",
      "- Instance 9733\n",
      "- Instance 9734\n",
      "- Instance 9735\n",
      "- Instance 9736\n",
      "- Instance 9737\n",
      "- Instance 9738\n",
      "- Instance 9739\n",
      "- Instance 9740\n",
      "- Instance 9741\n",
      "- Instance 9742\n",
      "- Instance 9743\n",
      "- Instance 9744\n",
      "- Instance 9745\n",
      "- Instance 9746\n",
      "- Instance 9747\n",
      "- Instance 9748\n",
      "- Instance 9749\n",
      "- Instance 9750\n",
      "- Instance 9751\n",
      "- Instance 9752\n",
      "- Instance 9753\n",
      "- Instance 9754\n",
      "- Instance 9755\n",
      "- Instance 9756\n",
      "- Instance 9757\n",
      "- Instance 9758\n",
      "- Instance 9759\n",
      "- Instance 9760\n",
      "- Instance 9761\n",
      "- Instance 9762\n",
      "- Instance 9763\n",
      "- Instance 9764\n",
      "- Instance 9765\n",
      "- Instance 9766\n",
      "- Instance 9767\n",
      "- Instance 9768\n",
      "- Instance 9769\n",
      "- Instance 9770\n",
      "- Instance 9771\n",
      "- Instance 9772\n",
      "- Instance 9773\n",
      "- Instance 9774\n",
      "- Instance 9775\n",
      "- Instance 9776\n",
      "- Instance 9777\n",
      "- Instance 9778\n",
      "- Instance 9779\n",
      "- Instance 9780\n",
      "- Instance 9781\n",
      "- Instance 9782\n",
      "- Instance 9783\n",
      "- Instance 9784\n",
      "- Instance 9785\n",
      "- Instance 9786\n",
      "- Instance 9787\n",
      "- Instance 9788\n",
      "- Instance 9789\n",
      "- Instance 9790\n",
      "- Instance 9791\n",
      "- Instance 9792\n",
      "- Instance 9793\n",
      "- Instance 9794\n",
      "- Instance 9795\n",
      "- Instance 9796\n",
      "- Instance 9797\n",
      "- Instance 9798\n",
      "- Instance 9799\n",
      "- Instance 9800\n",
      "- Instance 9801\n",
      "- Instance 9802\n",
      "- Instance 9803\n",
      "- Instance 9804\n",
      "- Instance 9805\n",
      "- Instance 9806\n",
      "- Instance 9807\n",
      "- Instance 9808\n",
      "- Instance 9809\n",
      "- Instance 9810\n",
      "- Instance 9811\n",
      "- Instance 9812\n",
      "- Instance 9813\n",
      "- Instance 9814\n",
      "- Instance 9815\n",
      "- Instance 9816\n",
      "- Instance 9817\n",
      "- Instance 9818\n",
      "- Instance 9819\n",
      "- Instance 9820\n",
      "- Instance 9821\n",
      "- Instance 9822\n",
      "- Instance 9823\n",
      "- Instance 9824\n",
      "- Instance 9825\n",
      "- Instance 9826\n",
      "- Instance 9827\n",
      "- Instance 9828\n",
      "- Instance 9829\n",
      "- Instance 9830\n",
      "- Instance 9831\n",
      "- Instance 9832\n",
      "- Instance 9833\n",
      "- Instance 9834\n",
      "- Instance 9835\n",
      "- Instance 9836\n",
      "- Instance 9837\n",
      "- Instance 9838\n",
      "- Instance 9839\n",
      "- Instance 9840\n",
      "- Instance 9841\n",
      "- Instance 9842\n",
      "- Instance 9843\n",
      "- Instance 9844\n",
      "- Instance 9845\n",
      "- Instance 9846\n",
      "- Instance 9847\n",
      "- Instance 9848\n",
      "- Instance 9849\n",
      "- Instance 9850\n",
      "- Instance 9851\n",
      "- Instance 9852\n",
      "- Instance 9853\n",
      "- Instance 9854\n",
      "- Instance 9855\n",
      "- Instance 9856\n",
      "- Instance 9857\n",
      "- Instance 9858\n",
      "- Instance 9859\n",
      "- Instance 9860\n",
      "- Instance 9861\n",
      "- Instance 9862\n",
      "- Instance 9863\n",
      "- Instance 9864\n",
      "- Instance 9865\n",
      "- Instance 9866\n",
      "- Instance 9867\n",
      "- Instance 9868\n",
      "- Instance 9869\n",
      "- Instance 9870\n",
      "- Instance 9871\n",
      "- Instance 9872\n",
      "- Instance 9873\n",
      "- Instance 9874\n",
      "- Instance 9875\n",
      "- Instance 9876\n",
      "- Instance 9877\n",
      "- Instance 9878\n",
      "- Instance 9879\n",
      "- Instance 9880\n",
      "- Instance 9881\n",
      "- Instance 9882\n",
      "- Instance 9883\n",
      "- Instance 9884\n",
      "- Instance 9885\n",
      "- Instance 9886\n",
      "- Instance 9887\n",
      "- Instance 9888\n",
      "- Instance 9889\n",
      "- Instance 9890\n",
      "- Instance 9891\n",
      "- Instance 9892\n",
      "- Instance 9893\n",
      "- Instance 9894\n",
      "- Instance 9895\n",
      "- Instance 9896\n",
      "- Instance 9897\n",
      "- Instance 9898\n",
      "- Instance 9899\n",
      "- Instance 9900\n",
      "- Instance 9901\n",
      "- Instance 9902\n",
      "- Instance 9903\n",
      "- Instance 9904\n",
      "- Instance 9905\n",
      "- Instance 9906\n",
      "- Instance 9907\n",
      "- Instance 9908\n",
      "- Instance 9909\n",
      "- Instance 9910\n",
      "- Instance 9911\n",
      "- Instance 9912\n",
      "- Instance 9913\n",
      "- Instance 9914\n",
      "- Instance 9915\n",
      "- Instance 9916\n",
      "- Instance 9917\n",
      "- Instance 9918\n",
      "- Instance 9919\n",
      "- Instance 9920\n",
      "- Instance 9921\n",
      "- Instance 9922\n",
      "- Instance 9923\n",
      "- Instance 9924\n",
      "- Instance 9925\n",
      "- Instance 9926\n",
      "- Instance 9927\n",
      "- Instance 9928\n",
      "- Instance 9929\n",
      "- Instance 9930\n",
      "- Instance 9931\n",
      "- Instance 9932\n",
      "- Instance 9933\n",
      "- Instance 9934\n",
      "- Instance 9935\n",
      "- Instance 9936\n",
      "- Instance 9937\n",
      "- Instance 9938\n",
      "- Instance 9939\n",
      "- Instance 9940\n",
      "- Instance 9941\n",
      "- Instance 9942\n",
      "- Instance 9943\n",
      "- Instance 9944\n",
      "- Instance 9945\n",
      "- Instance 9946\n",
      "- Instance 9947\n",
      "- Instance 9948\n",
      "- Instance 9949\n",
      "- Instance 9950\n",
      "- Instance 9951\n",
      "- Instance 9952\n",
      "- Instance 9953\n",
      "- Instance 9954\n",
      "- Instance 9955\n",
      "- Instance 9956\n",
      "- Instance 9957\n",
      "- Instance 9958\n",
      "- Instance 9959\n",
      "- Instance 9960\n",
      "- Instance 9961\n",
      "- Instance 9962\n",
      "- Instance 9963\n",
      "- Instance 9964\n",
      "- Instance 9965\n",
      "- Instance 9966\n",
      "- Instance 9967\n",
      "- Instance 9968\n",
      "- Instance 9969\n",
      "- Instance 9970\n",
      "- Instance 9971\n",
      "- Instance 9972\n",
      "- Instance 9973\n",
      "- Instance 9974\n",
      "- Instance 9975\n",
      "- Instance 9976\n",
      "- Instance 9977\n",
      "- Instance 9978\n",
      "- Instance 9979\n",
      "- Instance 9980\n",
      "- Instance 9981\n",
      "- Instance 9982\n",
      "- Instance 9983\n",
      "- Instance 9984\n",
      "- Instance 9985\n",
      "- Instance 9986\n",
      "- Instance 9987\n",
      "- Instance 9988\n",
      "- Instance 9989\n",
      "- Instance 9990\n",
      "- Instance 9991\n",
      "- Instance 9992\n",
      "- Instance 9993\n",
      "- Instance 9994\n",
      "- Instance 9995\n",
      "- Instance 9996\n",
      "- Instance 9997\n",
      "- Instance 9998\n",
      "- Instance 9999\n",
      "- Instance 10000\n"
     ]
    }
   ],
   "source": [
    "if maintenance_needed_indices.any():\n",
    "    print(\"Maintenance is predicted to be needed for the following instances:\")\n",
    "    for idx in maintenance_needed_indices:\n",
    "        print(f\"- Instance {idx + 1}\")\n",
    "else:\n",
    "    print(\"No maintenance needs predicted.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3: Data Engineering Specialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.1: Design and set up data pipelines to handle streaming data from IoT sensors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries for data processing and pipeline design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import random\n",
    "from datetime import datetime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulate streaming data from IoT sensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_sensor_data():\n",
    "    while True:\n",
    "        # Generate random sensor data\n",
    "        sensor_data = {\n",
    "            'timestamp': datetime.now(),\n",
    "            'temperature': round(random.uniform(20, 30), 2),\n",
    "            'pressure': round(random.uniform(900, 1100), 2),\n",
    "            'humidity': round(random.uniform(30, 70), 2)\n",
    "        }\n",
    "        yield sensor_data\n",
    "        \n",
    "        # Introduce a delay to simulate real-time data streaming\n",
    "        time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to process and store streaming data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_streaming_data():\n",
    "    # Initialize an empty list to store data\n",
    "    data_buffer = []\n",
    "    # Create a generator for streaming data\n",
    "    sensor_stream = generate_sensor_data()\n",
    "    try:\n",
    "        while True:\n",
    "            # Get the next data point from the sensor stream\n",
    "            sensor_data = next(sensor_stream)\n",
    "            # Append the data to the buffer\n",
    "            data_buffer.append(sensor_data)\n",
    "            # Process the data buffer (e.g., perform batch processing, clean and preprocess data)\n",
    "            if len(data_buffer) >= 10:\n",
    "                process_data(data_buffer)\n",
    "                # Clear the buffer after processing\n",
    "                data_buffer = []\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Streaming stopped.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to process batched data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_data(data):\n",
    "    # Convert the list of dictionaries to a DataFrame\n",
    "    df = pd.DataFrame(data)\n",
    "    # Perform data cleaning, preprocessing, and analysis\n",
    "    # For demonstration, we'll just print the data\n",
    "    print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Main function to start the data processing pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   timestamp  temperature  pressure  humidity\n",
      "0 2024-05-04 22:12:51.571798        26.95   1017.17     44.85\n",
      "1 2024-05-04 22:12:52.572129        25.19    949.39     43.27\n",
      "2 2024-05-04 22:12:53.573442        23.03   1065.04     68.30\n",
      "3 2024-05-04 22:12:54.575021        29.57    910.75     44.32\n",
      "4 2024-05-04 22:12:55.575150        29.73   1085.32     57.90\n",
      "5 2024-05-04 22:12:56.576348        28.07    956.57     68.73\n",
      "6 2024-05-04 22:12:57.581215        28.17   1045.16     68.00\n",
      "7 2024-05-04 22:12:58.582585        24.78    971.86     67.10\n",
      "8 2024-05-04 22:12:59.583568        24.22   1022.44     34.50\n",
      "9 2024-05-04 22:13:00.584223        25.54   1063.53     36.20\n",
      "                   timestamp  temperature  pressure  humidity\n",
      "0 2024-05-04 22:13:01.597665        27.29    911.93     54.08\n",
      "1 2024-05-04 22:13:02.597769        20.51    925.35     59.83\n",
      "2 2024-05-04 22:13:03.598265        21.23   1030.97     51.94\n",
      "3 2024-05-04 22:13:04.599524        26.40   1098.84     59.44\n",
      "4 2024-05-04 22:13:05.599674        23.41   1001.64     34.43\n",
      "5 2024-05-04 22:13:06.600737        24.79   1024.94     59.34\n",
      "6 2024-05-04 22:13:07.600860        26.70    924.68     42.60\n",
      "7 2024-05-04 22:13:08.603022        22.20   1099.82     44.33\n",
      "8 2024-05-04 22:13:09.604180        20.67    908.69     66.69\n",
      "9 2024-05-04 22:13:10.604285        21.03   1024.74     48.46\n",
      "Streaming stopped.\n"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    process_streaming_data()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.2: Implement data storage solutions (e.g., time-series databases) to efficiently store and retrieve machine data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sqlalchemy in c:\\users\\user\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (2.0.28)\n",
      "Requirement already satisfied: typing-extensions>=4.6.0 in c:\\users\\user\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from sqlalchemy) (4.9.0)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\user\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from sqlalchemy) (3.0.3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 23.3.2 -> 24.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "! pip install sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database 'machine_data' created successfully\n",
      "DataFrame stored in MySQL database successfully\n",
      "Database 'machine_data' created successfully\n",
      "DataFrame stored in MySQL database successfully\n",
      "Database 'machine_data' created successfully\n",
      "DataFrame stored in MySQL database successfully\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[27], line 82\u001b[0m\n\u001b[0;32m     79\u001b[0m store_dataframe_to_mysql(data, username, password, host, database_name, table_name)\n\u001b[0;32m     81\u001b[0m \u001b[38;5;66;03m# Sleep for some time before fetching new data again\u001b[39;00m\n\u001b[1;32m---> 82\u001b[0m \u001b[43mtime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Sleep for 10 seconds (adjust as needed)\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Importing necessary modules\n",
    "import mysql.connector\n",
    "from sqlalchemy import create_engine\n",
    "import pandas as pd\n",
    "import configparser\n",
    "import time\n",
    "import random\n",
    "from datetime import datetime\n",
    "\n",
    "# Function to create a MySQL database\n",
    "def create_database(host, username, password, database_name):\n",
    "    try:\n",
    "        # Connect to MySQL server\n",
    "        connection = mysql.connector.connect(\n",
    "            host=host,\n",
    "            user=username,\n",
    "            password=password\n",
    "        )\n",
    "\n",
    "        # Create a cursor object\n",
    "        cursor = connection.cursor()\n",
    "\n",
    "        # Execute an SQL query to create a database\n",
    "        cursor.execute(f\"CREATE DATABASE IF NOT EXISTS {database_name}\")\n",
    "        print(f\"Database '{database_name}' created successfully\")\n",
    "\n",
    "    except mysql.connector.Error as error:\n",
    "        print(\"Error creating database:\", error)\n",
    "\n",
    "    finally:\n",
    "        # Close the cursor and the connection\n",
    "        if 'connection' in locals():\n",
    "            cursor.close()\n",
    "            connection.close()\n",
    "\n",
    "# Function to generate random sensor data\n",
    "def generate_sensor_data():\n",
    "    timestamp = datetime.now()\n",
    "    temperature = round(random.uniform(20, 30), 2)\n",
    "    pressure = round(random.uniform(900, 1100), 2)\n",
    "    humidity = round(random.uniform(30, 70), 2)\n",
    "    return {'timestamp': timestamp, 'temperature': temperature, 'pressure': pressure, 'humidity': humidity}\n",
    "\n",
    "# Function to store DataFrame in MySQL database\n",
    "def store_dataframe_to_mysql(dataframe, username, password, host, database_name, table_name):\n",
    "    try:\n",
    "        # Create a connection to MySQL database\n",
    "        engine = create_engine(f'mysql+mysqlconnector://{username}:{password}@{host}/{database_name}')\n",
    "\n",
    "        # Store DataFrame in MySQL database\n",
    "        dataframe.to_sql(table_name, engine, if_exists='append', index=False)\n",
    "        print(\"DataFrame stored in MySQL database successfully\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"An error occurred:\", e)\n",
    "\n",
    "# Read MySQL connection details from config.ini\n",
    "config = configparser.ConfigParser()\n",
    "config.read('config.ini')\n",
    "\n",
    "username = config.get('mysql', 'user')\n",
    "password = config.get('mysql', 'password')\n",
    "host = 'localhost'\n",
    "database_name = 'machine_data'\n",
    "table_name = 'prediction_table'  # Specify your table name\n",
    "\n",
    "# Infinite loop to continuously fetch and store data\n",
    "while True:\n",
    "    # Generate random sensor data\n",
    "    sensor_data = generate_sensor_data()\n",
    "    \n",
    "    # Create DataFrame from sensor data\n",
    "    data = pd.DataFrame(sensor_data, index=[0])\n",
    "    \n",
    "    # Create the database if not exists\n",
    "    create_database(host, username, password, database_name)\n",
    "    \n",
    "    # Store DataFrame in MySQL database\n",
    "    store_dataframe_to_mysql(data, username, password, host, database_name, table_name)\n",
    "\n",
    "    # Sleep for some time before fetching new data again\n",
    "    time.sleep(10)  # Sleep for 10 seconds (adjust as needed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.3: Apply data cleaning techniques to ensure quality and reliability of the data used for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n",
      "Connection to MySQL DB successful\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n",
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_7156\\1704301163.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  data = pd.read_sql(query, connection)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data retrieval and saving completed.\n"
     ]
    }
   ],
   "source": [
    "import mysql.connector\n",
    "from mysql.connector import Error\n",
    "import pandas as pd\n",
    "import configparser\n",
    "\n",
    "# Function to establish connection to MySQL\n",
    "def create_connection(host_name, user_name, user_password, db_name):\n",
    "    connection = None\n",
    "    try:\n",
    "        connection = mysql.connector.connect(\n",
    "            host=host_name,\n",
    "            user=user_name,\n",
    "            passwd=user_password,\n",
    "            database=db_name\n",
    "        )\n",
    "        if connection.is_connected():\n",
    "            print(\"Connection to MySQL DB successful\")\n",
    "    except mysql.connector.Error as e:\n",
    "        print(f\"The error '{e}' occurred\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "\n",
    "    return connection\n",
    "\n",
    "# Function to retrieve data from MySQL database\n",
    "def retrieve_data(connection, table_name):\n",
    "    try:\n",
    "        # Construct SQL query to select all data from the specified table\n",
    "        query = f\"SELECT * FROM {table_name}\"\n",
    "        \n",
    "        # Execute SQL query and read data into a DataFrame\n",
    "        data = pd.read_sql(query, connection)\n",
    "        return data\n",
    "    except Exception as e:\n",
    "        # Handle any errors that occur during data retrieval\n",
    "        print(\"An error occurred while retrieving data:\", e)\n",
    "\n",
    "# Function to clean the data\n",
    "def clean_data(data):\n",
    "    # Handle missing values by removing rows with any NaN values\n",
    "    data.dropna(inplace=True)\n",
    "    \n",
    "    # Handle duplicate rows by removing duplicate entries\n",
    "    data.drop_duplicates(inplace=True)\n",
    "    \n",
    "    # Convert the 'timestamp' column to datetime format for consistency\n",
    "    data['timestamp'] = pd.to_datetime(data['timestamp'])\n",
    "    \n",
    "    # Handle outliers in all columns (except 'timestamp') by removing values outside 3 standard deviations from the mean\n",
    "    for column in data.columns:\n",
    "        if column != 'timestamp':\n",
    "            mean = data[column].mean()\n",
    "            std = data[column].std()\n",
    "            data = data[(data[column] >= mean - 3 * std) & (data[column] <= mean + 3 * std)]\n",
    "\n",
    "    # Return the cleaned DataFrame\n",
    "    return data\n",
    "\n",
    "def main():\n",
    "    # Load MySQL connection details from .ini file\n",
    "    config = configparser.ConfigParser()\n",
    "    config.read('config.ini')\n",
    "\n",
    "    host_name = config['mysql']['host']\n",
    "    user_name = config['mysql']['user']\n",
    "    user_password = config['mysql']['password']\n",
    "    db_name = config['mysql']['database']\n",
    "\n",
    "    # List to store all cleaned data (optional for accumulating data)\n",
    "    all_cleaned_data = []\n",
    "\n",
    "    # Retrieve data 10 times\n",
    "    for _ in range(10):\n",
    "        # Establish connection to MySQL\n",
    "        connection = create_connection(host_name, user_name, user_password, db_name)\n",
    "\n",
    "        if connection:\n",
    "            # Retrieve data from MySQL database using established connection and specified table name\n",
    "            table_name = \"prediction_table\"\n",
    "            data = retrieve_data(connection, table_name)\n",
    "\n",
    "            # Clean the retrieved data\n",
    "            cleaned_data = clean_data(data)\n",
    "\n",
    "            # Optionally append cleaned data to list for accumulation\n",
    "            all_cleaned_data.append(cleaned_data)\n",
    "\n",
    "            # Close connection\n",
    "            connection.close()\n",
    "        else:\n",
    "            print(\"Failed to establish connection to MySQL\")\n",
    "\n",
    "    # Save data to CSV (use the accumulated data or the last cleaned_data)\n",
    "    if all_cleaned_data:  # If data was accumulated\n",
    "        # Concatenate all DataFrames (assuming similar structure)\n",
    "        final_cleaned_data = pd.concat(all_cleaned_data, ignore_index=True)\n",
    "        final_cleaned_data.to_csv(\"cleaned_machine_data.csv\", index=False)\n",
    "    else:\n",
    "        # If no data was retrieved, use the last cleaned_data (if available)\n",
    "        if cleaned_data is not None:\n",
    "            cleaned_data.to_csv(\"cleaned_machine_data.csv\", index=False)\n",
    "        else:\n",
    "            print(\"No data retrieved to save to CSV.\")\n",
    "\n",
    "    print(\"Data retrieval and saving completed.\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 4: End-to-End Project Execution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.1: Project planning, including timelines, resource allocation, and defining milestones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plan prioritizes key tasks to develop a basic functional prototype within a 7-day timeframe.\n",
    "\n",
    "1. Timeline:\n",
    "\n",
    "* Model Development & Training (4 days):\n",
    "\n",
    "Day 1: Review existing data analysis and identify features for model development.\n",
    "\n",
    "Day 2 & 3: Select a suitable machine learning model based on data and prediction goals (e.g., classification for predicting failures, regression for predicting remaining useful life). Train the model on your cleaned data retrieved from MySQL.\n",
    "\n",
    "Day 4: Evaluate model performance using relevant metrics. Refine the model with hyperparameter tuning (basic adjustments) if needed.\n",
    "\n",
    "* Deployment Preparation (1 day):\n",
    "\n",
    "Day 5: Choose a model deployment framework like TensorFlow Serving or a simpler option like scikit-learn for basic deployment.\n",
    "\n",
    "Set up the infrastructure for deployment in a controlled environment (e.g., separate server or container).\n",
    "\n",
    "* Testing & Validation (2 days):\n",
    "\n",
    "Day 6: Test the deployed model with a separate test dataset to ensure functionality and identify potential issues.\n",
    "\n",
    "Day 7: Refine the model or deployment configuration based on test results (basic adjustments).\n",
    "\n",
    "Develop a basic dashboard with a tool like Grafana to visualize predictions for initial validation.\n",
    "\n",
    "2. Resource Allocation:\n",
    "\n",
    "* Machine Learning Engineer (Focus): You will primarily focus on model development, training, and deployment.\n",
    "\n",
    "* Data Scientist (Support, if available): If available, a data scientist can assist with feature selection and model evaluation.\n",
    "\n",
    "3. Milestones:\n",
    "\n",
    "Day 4: Complete model training and initial performance evaluation.\n",
    "\n",
    "Day 5: Set up the deployment environment and basic model serving configuration.\n",
    "\n",
    "Day 7: Functional prototype with model deployment in a controlled environment and basic visualization dashboard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: Data acquisition from IoT devices, ensuring robust and secure data flows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Goal: Securely collect sensor data from IoT devices.\n",
    "\n",
    "* Ideal Approach (if possible): Real-time streaming using Apache Kafka, Amazon Kinesis, or MQTT for continuous data collection.\n",
    "\n",
    "* 7-Day Plan Alternative: Periodic data extraction from MySQL using existing script (e.g., hourly).\n",
    "\n",
    "* Security Measures:\n",
    "\n",
    "a. Authentication/Authorization to restrict access.\n",
    "\n",
    "b. Data Encryption (e.g., AES, TLS) for transmission and storage.\n",
    "\n",
    "c. Data Integrity checks (checksums, digital signatures).\n",
    "\n",
    "* Additional Considerations: Data pre-processing and system scalability for future growth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: Develop and tune machine learning models based on the cleaned data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: We have already cleaned and stored the data in \"cleaned_machine_data. Now we can proceed to building the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and explore the data\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data = pd.read_csv(\"cleaned_machine_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             timestamp  temperature  pressure  humidity\n",
      "0  2024-05-03 21:04:08        25.59   1025.39     44.19\n",
      "1  2024-05-03 21:05:18        20.98    929.89     32.97\n",
      "2  2024-05-03 21:06:18        20.41    923.60     33.40\n",
      "3  2024-05-03 21:07:09        21.09    985.19     69.43\n",
      "4  2024-05-03 21:07:20        23.72    966.63     45.46\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   timestamp    300 non-null    object \n",
      " 1   temperature  300 non-null    float64\n",
      " 2   pressure     300 non-null    float64\n",
      " 3   humidity     300 non-null    float64\n",
      "dtypes: float64(3), object(1)\n",
      "memory usage: 9.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.head())  # View the first few rows\n",
    "print(data.info())  # View data types and summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:993: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 980, in _score\n",
      "    scores = scorer(estimator, X_test, **score_params)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "TypeError: _BaseScorer.__call__() missing 1 required positional argument: 'y_true'\n",
      "\n",
      "  warnings.warn(\n",
      "c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [nan nan nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Potential anomalies (in filtered data):\n",
      "               timestamp  temperature  pressure  humidity  temp_diff\n",
      "1    2024-05-03 21:05:18    -1.138611 -1.309456 -1.148535  -1.162085\n",
      "2    2024-05-03 21:06:18    -1.332576 -1.428029 -1.113579  -0.144844\n",
      "3    2024-05-03 21:07:09    -1.101179 -0.266994  1.815338   0.169897\n",
      "4    2024-05-03 21:07:20    -0.206216 -0.616869 -0.133209   0.660892\n",
      "5    2024-05-03 21:07:30     1.859346 -0.999545  1.471477   1.527058\n",
      "..                   ...          ...       ...       ...        ...\n",
      "295  2024-05-03 21:46:53     1.005217  1.109698 -0.994082   1.670580\n",
      "296  2024-05-03 21:47:03    -0.958257 -1.079662  0.784567  -1.454165\n",
      "297  2024-05-04 22:13:37     0.964382 -0.220809  0.852852   1.421305\n",
      "298  2024-05-04 22:13:47    -0.107532 -0.254363 -1.014404  -0.794468\n",
      "299  2024-05-04 22:13:57     0.964382 -0.549194 -0.770531   0.791824\n",
      "\n",
      "[299 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Feature engineering \n",
    "data[\"temp_diff\"] = data[\"temperature\"].diff()  # Difference between consecutive temperature readings\n",
    "\n",
    "# 2. Feature scaling\n",
    "data[\"temp_diff\"] = data[\"temperature\"].diff()  # Difference between consecutive temperature readings\n",
    "\n",
    "# 2. Feature scaling (optional)\n",
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(data[[\"temperature\", \"pressure\", \"humidity\", \"temp_diff\"]])\n",
    "data[[\"temperature\", \"pressure\", \"humidity\", \"temp_diff\"]] = scaled_features\n",
    "\n",
    "# Handle missing values (dropping rows with NaN)\n",
    "data_filtered = data.dropna()  # Drop rows with any missing values (consider imputation if data loss is critical)\n",
    "\n",
    "# Anomaly detection with hyperparameter tuning\n",
    "# Define a parameter grid for contamination values\n",
    "param_grid = {'contamination': [0.005, 0.01, 0.02]}\n",
    "\n",
    "# Create an Isolation Forest model\n",
    "model = IsolationForest()\n",
    "\n",
    "# Create a GridSearchCV object\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='f1')\n",
    "\n",
    "# Train the model on the filtered data\n",
    "grid_search.fit(data_filtered[[\"temperature\", \"pressure\", \"humidity\", \"temp_diff\"]])\n",
    "\n",
    "# Get the best model based on the chosen scoring metric (e.g., 'f1')\n",
    "best_model = grid_search.best_estimator_\n",
    "\n",
    "# Get anomaly scores using the best model\n",
    "anomaly_scores = best_model.decision_function(data_filtered[[\"temperature\", \"pressure\", \"humidity\", \"temp_diff\"]])\n",
    "\n",
    "# Set anomaly threshold \n",
    "threshold = -0.5 \n",
    "\n",
    "# Identify anomalies in the filtered data\n",
    "anomalies = data_filtered[anomaly_scores > threshold]\n",
    "\n",
    "# Print or visualize anomalies (consider mapping anomalies back to original data if needed)\n",
    "print(\"Potential anomalies (in filtered data):\")\n",
    "print(anomalies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.4: Deploy models into a production environment, setting up a system for continuous learning and model updating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a. The Data \"cleaned_machine_data.csv\" is uploaded on Google Cloud storage for further training of the ML model.\n",
    "\n",
    "b. Pipelines created on Google Cloud's Vertex AI platform.\n",
    "\n",
    "c. Pipeline run: \n",
    "* Template used Tabular classification/regression\n",
    "* Run name: test-1-anomalies-ml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 5: Tools and Technologies for Project Execution and Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.1: Identify and deploy tools and technologies for development and deployment, such as Python, R, TensorFlow, PySpark, Hadoop, and Kubernetes."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "Development & Deployment with Vertex AI\n",
    "* Used Python for development within Vertex AI Notebooks/scripts\n",
    "* Leveraged libraries like TensorFlow/scikit-learn to build the model\n",
    "* Explored Vertex AI Pipelines for potential future retraining automation\n",
    "* Deployed model on Vertex AI Endpoints for real-time/batch predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.2: Implement dashboards and monitoring systems using tools like Grafana or Kibana to visualize data and predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Visualize sensor data, anomaly predictions, and key metrics for system health insights.\n",
    "\n",
    "Steps:\n",
    "\n",
    "a. Collect sensor data and anomaly scores from deployed model (Vertex AI).\n",
    "\n",
    "b. Choose a data storage solution (Cloud SQL, TimescaleDB, Cloud Storage).\n",
    "\n",
    "c. Connect Grafana to the chosen data source.\n",
    "\n",
    "d. Build dashboards to visualize:\n",
    "\n",
    "* Sensor data over time.\n",
    "* Anomaly score trends.\n",
    "* Sensor-anomaly score correlations.\n",
    "* Alerts for significant anomaly detections.\n",
    "\n",
    "Benefits:\n",
    "\n",
    "a. Improved monitoring of system health and anomalies.\n",
    "\n",
    "b. Faster detection of potential issues.\n",
    "\n",
    "c. Data-driven decision making for maintenance and resource allocation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.3: Address contemporary challenges such as real-time data processing and ensuring data security and privacy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Challenge 1: Real-Time Data Processing\n",
    "\n",
    "Issue: Handling massive real-time sensor data for timely anomaly detection.\n",
    "\n",
    "Solutions:\n",
    "\n",
    "* Distributed processing frameworks (e.g., Apache Kafka, Spark Streaming)\n",
    "* Elastic and scalable infrastructure (cloud, containers)\n",
    "* Complex event processing (CEP) engines\n",
    "\n",
    "Challenge 2: Data Security and Privacy\n",
    "\n",
    "Issue: Protecting sensitive sensor data and user privacy.\n",
    "\n",
    "Solutions:\n",
    "\n",
    "* Encryption (data at rest and in transit)\n",
    "* Access controls\n",
    "* Real-time monitoring for suspicious activity\n",
    "* Compliance with data privacy regulations"
   ]
  }
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