Job Satisfaction Analysis/.ipynb_checkpoints/JobSatisfaction-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "eOEX0amSNBuA"
   },
   "outputs": [],
   "source": [
    "#imorting neccessory libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0FRoQw7_O5fu",
    "outputId": "fbb15331-be13-4390-b5b9-b11ee41d173b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running in local system\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SHRISTI\\AppData\\Local\\Temp\\ipykernel_6840\\2309029362.py:16: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df=pd.read_csv(r'C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\survey_results_public_2018.csv')\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "  import google.colab\n",
    "  IN_COLAB = True\n",
    "except:\n",
    "  IN_COLAB = False\n",
    "\n",
    "if IN_COLAB:\n",
    "    print(\"Running in google colab\\n\")\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    df=pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')\n",
    "\n",
    "else:\n",
    "    print(\"Running in local system\")\n",
    "    file_path = r\"C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\Stackoverflow-Analysis\"  # Replace with your file path\n",
    "    df=pd.read_csv(r'C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\survey_results_public_2018.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3y-4rilZPa-4",
    "outputId": "8d10e4d5-3240-4709-ec96-12c4031884db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Respondent Hobby OpenSource         Country         Student  \\\n",
      "0           1   Yes         No           Kenya              No   \n",
      "1           3   Yes        Yes  United Kingdom              No   \n",
      "2           4   Yes        Yes   United States              No   \n",
      "3           5    No         No   United States              No   \n",
      "4           7   Yes         No    South Africa  Yes, part-time   \n",
      "\n",
      "           Employment                                    FormalEducation  \\\n",
      "0  Employed part-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
      "1  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
      "2  Employed full-time                                   Associate degree   \n",
      "3  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
      "4  Employed full-time  Some college/university study without earning ...   \n",
      "\n",
      "                                      UndergradMajor  \\\n",
      "0                          Mathematics or statistics   \n",
      "1  A natural science (ex. biology, chemistry, phy...   \n",
      "2  Computer science, computer engineering, or sof...   \n",
      "3  Computer science, computer engineering, or sof...   \n",
      "4  Computer science, computer engineering, or sof...   \n",
      "\n",
      "                CompanySize  \\\n",
      "0        20 to 99 employees   \n",
      "1  10,000 or more employees   \n",
      "2        20 to 99 employees   \n",
      "3      100 to 499 employees   \n",
      "4  10,000 or more employees   \n",
      "\n",
      "                                             DevType  ...  \\\n",
      "0                               Full-stack developer  ...   \n",
      "1  Database administrator;DevOps specialist;Full-...  ...   \n",
      "2           Engineering manager;Full-stack developer  ...   \n",
      "3                               Full-stack developer  ...   \n",
      "4  Data or business analyst;Desktop or enterprise...  ...   \n",
      "\n",
      "                     Exercise Gender         SexualOrientation  \\\n",
      "0        3 - 4 times per week   Male  Straight or heterosexual   \n",
      "1   Daily or almost every day   Male  Straight or heterosexual   \n",
      "2                         NaN    NaN                       NaN   \n",
      "3  I don't typically exercise   Male  Straight or heterosexual   \n",
      "4        3 - 4 times per week   Male  Straight or heterosexual   \n",
      "\n",
      "                                    EducationParents  \\\n",
      "0           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
      "1           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
      "2                                                NaN   \n",
      "3  Some college/university study without earning ...   \n",
      "4  Some college/university study without earning ...   \n",
      "\n",
      "                  RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
      "0   Black or of African descent  25 - 34 years old        Yes         NaN   \n",
      "1  White or of European descent  35 - 44 years old        Yes         NaN   \n",
      "2                           NaN                NaN        NaN         NaN   \n",
      "3  White or of European descent  35 - 44 years old         No          No   \n",
      "4  White or of European descent  18 - 24 years old        Yes         NaN   \n",
      "\n",
      "                          SurveyTooLong     SurveyEasy  \n",
      "0  The survey was an appropriate length      Very easy  \n",
      "1  The survey was an appropriate length  Somewhat easy  \n",
      "2                                   NaN            NaN  \n",
      "3  The survey was an appropriate length  Somewhat easy  \n",
      "4  The survey was an appropriate length  Somewhat easy  \n",
      "\n",
      "[5 rows x 129 columns]\n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of the dataframe\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "smR6ZcZ6OIzF",
    "outputId": "89365fe3-5451-4578-de35-68c8d3ba8e4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Respondent    AssessJob1    AssessJob2    AssessJob3    AssessJob4  \\\n",
      "count   98855.000000  66985.000000  66985.000000  66985.000000  66985.000000   \n",
      "mean    50822.971635      6.397089      6.673524      5.906875      4.065791   \n",
      "std     29321.650410      2.788428      2.531202      2.642734      2.541196   \n",
      "min         1.000000      1.000000      1.000000      1.000000      1.000000   \n",
      "25%     25443.500000      4.000000      5.000000      4.000000      2.000000   \n",
      "50%     50823.000000      7.000000      7.000000      6.000000      4.000000   \n",
      "75%     76219.500000      9.000000      9.000000      8.000000      6.000000   \n",
      "max    101592.000000     10.000000     10.000000     10.000000     10.000000   \n",
      "\n",
      "         AssessJob5    AssessJob6    AssessJob7    AssessJob8    AssessJob9  \\\n",
      "count  66985.000000  66985.000000  66985.000000  66985.000000  66985.000000   \n",
      "mean       3.953243      4.407196      5.673181      4.225200      7.640009   \n",
      "std        2.520499      2.502069      2.923998      2.507411      2.407457   \n",
      "min        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
      "25%        2.000000      2.000000      3.000000      2.000000      6.000000   \n",
      "50%        3.000000      4.000000      6.000000      4.000000      8.000000   \n",
      "75%        6.000000      6.000000      8.000000      6.000000     10.000000   \n",
      "max       10.000000     10.000000     10.000000     10.000000     10.000000   \n",
      "\n",
      "       ...  JobEmailPriorities6  JobEmailPriorities7  ConvertedSalary  \\\n",
      "count  ...          46213.00000         46213.000000     4.770200e+04   \n",
      "mean   ...              4.97425             4.836388     9.578086e+04   \n",
      "std    ...              1.86063             1.659844     2.023482e+05   \n",
      "min    ...              1.00000             1.000000     0.000000e+00   \n",
      "25%    ...              4.00000             4.000000     2.384400e+04   \n",
      "50%    ...              5.00000             5.000000     5.507500e+04   \n",
      "75%    ...              7.00000             6.000000     9.300000e+04   \n",
      "max    ...              7.00000             7.000000     2.000000e+06   \n",
      "\n",
      "       AdsPriorities1  AdsPriorities2  AdsPriorities3  AdsPriorities4  \\\n",
      "count    60479.000000    60479.000000    60479.000000    60479.000000   \n",
      "mean         2.726880        3.805784        3.340945        3.782470   \n",
      "std          1.881078        1.821323        1.673485        1.844864   \n",
      "min          1.000000        1.000000        1.000000        1.000000   \n",
      "25%          1.000000        2.000000        2.000000        2.000000   \n",
      "50%          2.000000        4.000000        3.000000        4.000000   \n",
      "75%          4.000000        5.000000        5.000000        5.000000   \n",
      "max          7.000000        7.000000        7.000000        7.000000   \n",
      "\n",
      "       AdsPriorities5  AdsPriorities6  AdsPriorities7  \n",
      "count    60479.000000    60479.000000    60479.000000  \n",
      "mean         4.383604        5.138809        4.821459  \n",
      "std          1.931746        1.853249        1.874895  \n",
      "min          1.000000        1.000000        1.000000  \n",
      "25%          3.000000        4.000000        3.000000  \n",
      "50%          5.000000        6.000000        5.000000  \n",
      "75%          6.000000        7.000000        7.000000  \n",
      "max          7.000000        7.000000        7.000000  \n",
      "\n",
      "[8 rows x 42 columns]\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bvc8e8D7PfFC",
    "outputId": "4f08b6d0-73ca-40cc-c70b-5a0b0b70fdb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent           0\n",
      "Hobby                0\n",
      "OpenSource           0\n",
      "Country            412\n",
      "Student           3954\n",
      "                 ...  \n",
      "Age              34281\n",
      "Dependents       36259\n",
      "MilitaryUS       83074\n",
      "SurveyTooLong    32914\n",
      "SurveyEasy       32976\n",
      "Length: 129, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "9SdbszuTPfXT",
    "outputId": "1d71f563-51e2-45fc-e154-0d925b9624f5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAIjCAYAAAC+ktLwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABw2klEQVR4nO3dZ3gV1eL+/Xun99BSJQbpvRgVAgIqJQgiKEoRJSCgNKXL4aiAeDwoqGBBPRbAAoqIoBSRDgqhCIROKAZRIYC0EEoCyXpe8GR+2aQQYjBh/t/PdeWCPbP2zJpZM7Pn3rNnjcMYYwQAAAAAsAWXoq4AAAAAAKDwEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwAAAABshJAHAAAAADZCyAMAAAAAGyHkAQAAAICNEPIAwAbGjBkjh8Ohv/76q6irck2Zdb1eR48e1SOPPKLSpUvL4XBo0qRJhV+5XBS0zkXh4MGDcjgcmjZt2g2f17Rp0+RwOHTw4EFrWLly5fTAAw/c8HlL0sqVK+VwOLRy5cp/ZH4AcLMg5AFAMZJ50vzLL7/84/OeN2+emjZtquDgYPn4+Kh8+fLq2LGjFi1adN3TOn/+vMaMGVOoJ9+DBw/Wjz/+qJEjR+rzzz9Xq1atCm3a0o2pc2FwOBzWn5ubm0qVKqWoqCgNHDhQu3btKrT5vPfee/9IMCyI4lw3ACiOHMYYU9SVAABcMW3aNPXo0UMbN27UHXfcke/3jRkzRi+99JKOHz+uMmXKXPd8X3/9dQ0fPlxNmzZVu3bt5OPjo/3792vp0qWqU6fOdZ9g//XXXwoKCtLo0aM1ZswYp3GXL1/W5cuX5eXldV3TDA0NVfPmzfXFF19c1/vy60bUuTA4HA61aNFC3bp1kzFGZ86c0datWzVr1iydO3dOr732moYMGWKVN8YoNTVV7u7ucnV1zfd8atasqTJlylxXyE1PT9elS5fk6elpXeksV66catasqfnz5+d7OgWtW0ZGhtLS0uTh4SEXF763BoBMbkVdAQBA0bp8+bJefvlltWjRQosXL842/tixY4U6Pzc3N7m5Xf/Hz7Fjx1SiRIlCrUt+FbTOhaVy5cp6/PHHnYa9+uqratu2rYYOHaqqVauqdevWkq6EwhsdRs+dOydfX1+5urpeV5AsbC4uLkUSvAGguONrLwAo5pYvX67GjRvL19dXJUqUULt27bR79+4cy/7111/q2LGjAgICVLp0aQ0cOFAXL17Mc/p//fWXkpOT1ahRoxzHBwcHW/9PS0vTqFGjFBUVpcDAQPn6+qpx48ZasWKFVebgwYMKCgqSJL300kvWTw0zr47ldH/bkiVLdPfdd6tEiRLy8/NTlSpV9O9//1vS//2E1RijyZMnW9OTpJMnT2rYsGGqVauW/Pz8FBAQoPvvv19bt27NthwXL17UmDFjVLlyZXl5eSksLEwPP/ywDhw4UKA6Z4bjChUqyNPTU+XKldO///1vpaamOpXLvEft559/1l133SUvLy+VL19en332WZ7tci2lS5fWV199JTc3N73yyivW8JzuyUtKSlKPHj1UtmxZeXp6KiwsTO3atbPupStXrpx27typVatWWct+zz33SPq/9b9q1Sr169dPwcHBKlu2rNO4rPfkZVq8eLHq1q0rLy8vVa9eXd9++63T+Nzuc7x6mnnVLbd78mbNmqWoqCh5e3urTJkyevzxx/Xnn386lenevbv8/Pz0559/qn379vLz81NQUJCGDRum9PT0a6x9ACjeuJIHAMXY0qVLdf/996t8+fIaM2aMLly4oHfeeUeNGjXS5s2bVa5cOafyHTt2VLly5TRu3DitW7dOb7/9tk6dOpVnoAgODpa3t7fmzZunZ555RqVKlcq1bHJysj7++GN16dJFvXv31tmzZ/XJJ58oJiZGGzZsUN26dRUUFKT3339fffv21UMPPaSHH35YklS7du0cp7lz50498MADql27tsaOHStPT0/t379fa9askSQ1adJEn3/+uZ544gnrZ4uZfv31V82dO1ePPvqobrvtNh09elT/+9//1LRpU+3atUvh4eGSrvys8IEHHtCyZcvUuXNnDRw4UGfPntWSJUu0Y8cONW/e/LrqLEm9evXSp59+qkceeURDhw7V+vXrNW7cOO3evVtz5sxxKrt//3498sgj6tmzp2JjYzVlyhR1795dUVFRqlGjRq7zuJZbb71VTZs21YoVK5ScnKyAgIAcy3Xo0EE7d+7UM888o3LlyunYsWNasmSJDh06pHLlymnSpEl65pln5Ofnp+eff16SFBIS4jSNfv36KSgoSKNGjdK5c+fyrNe+ffvUqVMn9enTR7GxsZo6daoeffRRLVq0SC1atLiuZcxP3bLK/MnznXfeqXHjxuno0aN66623tGbNGm3ZssXpanB6erpiYmJUv359vf7661q6dKneeOMNVahQQX379r2uegJAsWIAAMXG1KlTjSSzceNGY4wxdevWNcHBwebEiRNWma1btxoXFxfTrVs3a9jo0aONJPPggw86Ta9fv35Gktm6dWue8x01apSRZHx9fc39999vXnnlFbNp06Zs5S5fvmxSU1Odhp06dcqEhISYJ5980hp2/PhxI8mMHj062zQy65pp4sSJRpI5fvx4nnWUZPr37+807OLFiyY9Pd1pWGJiovH09DRjx461hk2ZMsVIMm+++Wa26WZkZFx3nePj440k06tXL6dyw4YNM5LM8uXLrWGRkZFGklm9erU17NixY8bT09MMHTo0z2XObbmzGjhwoFMbJyYmGklm6tSpxpgr7SPJTJgwIc/51KhRwzRt2jTb8Mxt8u677zaXL1/OcVxiYqI1LHN5Z8+ebQ07c+aMCQsLM/Xq1bOGXb1O85pmbnVbsWKFkWRWrFhhjDEmLS3NBAcHm5o1a5oLFy5Y5ebPn28kmVGjRlnDYmNjjSSn7cQYY+rVq2eioqKyzQsAbib8XBMAiqkjR44oPj5e3bt3d7q6Vrt2bbVo0UILFy7M9p7+/fs7vX7mmWckKceyWb300kuaMWOG6tWrpx9//FHPP/+8oqKidPvttzv9NNTV1VUeHh6SrnR6cfLkSV2+fFl33HGHNm/eXKDlzLyy8t133ykjI+O63uvp6Wl1uJGenq4TJ05YP/fMWp/Zs2erTJky1vrIqiCPRshcn1k7PJGkoUOHSpIWLFjgNLx69epq3Lix9TooKEhVqlTRr7/+et3zvpqfn58k6ezZszmO9/b2loeHh1auXKlTp04VeD69e/fO9/134eHheuihh6zXAQEB6tatm7Zs2aKkpKQC1+FafvnlFx07dkz9+vVzulevTZs2qlq1arZ2kaQ+ffo4vW7cuHGhtAsAFCVCHgAUU7/99pskqUqVKtnGVatWTX/99Ve2n81VqlTJ6XWFChXk4uKS4z1TV+vSpYt++uknnTp1SosXL9Zjjz2mLVu2qG3btk739X366aeqXbu2vLy8VLp0aQUFBWnBggU6c+ZMAZZS6tSpkxo1aqRevXopJCREnTt31tdff52vwJeRkaGJEyeqUqVK8vT0VJkyZRQUFKRt27Y51efAgQOqUqVKoXWe8ttvv8nFxUUVK1Z0Gh4aGqoSJUpYbZfp1ltvzTaNkiVL/q3QlSklJUWS5O/vn+N4T09Pvfbaa/rhhx8UEhKiJk2aaPz48dcdtm677bZ8l61YsWK28Fy5cmVJyte2WFB57TNVq1bN1i5eXl7WvZiZCqtdAKAoEfIAwMYKcpUqICBALVq00PTp0xUbG6sDBw5o/fr1kqQvvvhC3bt3V4UKFfTJJ59o0aJFWrJkie67777rvgqXydvbW6tXr9bSpUv1xBNPaNu2berUqZNatGhxzQ4w/vvf/2rIkCFq0qSJvvjiC/34449asmSJatSoUeD6XI/8rt/croCZQniK0Y4dO+Tq6ppnCBs0aJD27t2rcePGycvLSy+++KKqVaumLVu25Hs+3t7ef7uuWeW27v7JTk+KsmdQALiRCHkAUExFRkZKkhISErKN27Nnj8qUKSNfX1+n4fv27XN6vX//fmVkZGTroCW/Mp/Vd+TIEUnSN998o/Lly+vbb7/VE088oZiYGDVv3jxbD57XGy5dXFzUrFkzvfnmm9q1a5deeeUVLV++3KnXzpx88803uvfee/XJJ5+oc+fOatmypZo3b67Tp087latQoYISEhJ06dKlXKd1PXWOjIxURkZGtvV99OhRnT592mq7G+3QoUNatWqVoqOjc72Sl6lChQoaOnSoFi9erB07digtLU1vvPGGNb4gXwjkZv/+/dkC7N69eyXJ2hZLliwpSdna6uqrbddTt7z2mYSEhH+sXQCgqBHyAKCYCgsLU926dfXpp586nQjv2LFDixcvtp6LltXkyZOdXr/zzjuSpPvvvz/X+Zw/f15xcXE5jvvhhx8k/d/P3zKvfGQ9gV+/fn229/v4+EjKfgKfk5MnT2YbVrduXUnK9jiCq7m6umYLE7NmzcrWXX6HDh30119/6d133802jcz3X0+dM9f9pEmTnIa/+eabkq7cA3ajnTx5Ul26dFF6errV62ROzp8/ny2EV6hQQf7+/k7r19fXN1/Lnh+HDx926mE0OTlZn332merWravQ0FCrDpK0evVqq9y5c+f06aefZptefut2xx13KDg4WB988IHTsv3www/avXv3P9IuAFAc8AgFACjGJkyYoPvvv1/R0dHq2bOn9QiFwMBA6xluWSUmJurBBx9Uq1atFBcXpy+++EKPPfaY6tSpk+s8zp8/r4YNG6pBgwZq1aqVIiIidPr0ac2dO1c//fST2rdvr3r16kmSHnjgAX377bd66KGH1KZNGyUmJuqDDz5Q9erVrXvDpCs/7atevbpmzpypypUrq1SpUqpZs6Zq1qyZbf5jx47V6tWr1aZNG0VGRurYsWN67733VLZsWd199915rp8HHnhAY8eOVY8ePdSwYUNt375d06dPV/ny5Z3KdevWTZ999pmGDBmiDRs2qHHjxjp37pyWLl2qfv36qV27dtdV5zp16ig2NlYffvihTp8+raZNm2rDhg369NNP1b59e91777151vt67d27V1988YWMMUpOTtbWrVs1a9YspaSk6M0331SrVq3yfG+zZs3UsWNHVa9eXW5ubpozZ46OHj2qzp07W+WioqL0/vvv6z//+Y8qVqyo4OBg3XfffQWqb+XKldWzZ09t3LhRISEhmjJlio4ePaqpU6daZVq2bKlbb71VPXv21PDhw+Xq6qopU6YoKChIhw4dcppefuvm7u6u1157TT169FDTpk3VpUsX6xEK5cqV0+DBgwu0PABw0ynKrj0BAM4yu/rfvHmzNWzp0qWmUaNGxtvb2wQEBJi2bduaXbt2Ob0vszv6Xbt2mUceecT4+/ubkiVLmgEDBjh1JZ+TS5cumY8++si0b9/eREZGGk9PT+Pj42Pq1atnJkyY4PTIhIyMDPPf//7XKlevXj0zf/58ExsbayIjI52mu3btWhMVFWU8PDycHk1wddf5y5YtM+3atTPh4eHGw8PDhIeHmy5dupi9e/c6TU+5PEJh6NChJiwszHh7e5tGjRqZuLg407Rp02xd7p8/f948//zz5rbbbjPu7u4mNDTUPPLII+bAgQPXXefM9fbSSy9Z04uIiDAjR440Fy9edCoXGRlp2rRpk22951THnEiy/lxcXEyJEiVMvXr1zMCBA83OnTuzlb/6EQp//fWX6d+/v6latarx9fU1gYGBpn79+ubrr792el9SUpJp06aN8ff3N5Ksul39WI+scnuEQps2bcyPP/5oateubTw9PU3VqlXNrFmzsr1/06ZNpn79+sbDw8Pceuut5s0338xxmrnV7epHKGSaOXOmqVevnvH09DSlSpUyXbt2NX/88YdTmdjYWOPr65utTrk92gEAbiYOYwrhrm8AQKF4++23NXDgQO3fv9/6ORsAAMD14J48AChGNm7cKF9fXzqIAAAABcY9eQBQDMyePVsrV67U9OnT1atXr0J7nhsAAPh/Dz/XBIBi4LbbbtPZs2f10EMPadKkSdkejQAAAJBfhDwAAAAAsBHuyQMAAAAAGyHkAQAAAICNcGd/IcnIyNDhw4fl7+8vh8NR1NUBAAAAUESMMTp79qzCw8Pl4vLPX1cj5BWSw4cPKyIioqirAQAAAKCY+P3331W2bNl/fL6EvELi7+8v6UpDBgQEFHFtAAAAABSV5ORkRUREWBnhn0bIKySZP9EMCAgg5AEAAAAostu46HgFAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbcSvqCgDAPyVq+GdFXQVb2zShW1FXAQAAiCt5AAAAAGArhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGykSEPeuHHjdOedd8rf31/BwcFq3769EhISnMrcc889cjgcTn99+vRxKnPo0CG1adNGPj4+Cg4O1vDhw3X58mWnMitXrtTtt98uT09PVaxYUdOmTctWn8mTJ6tcuXLy8vJS/fr1tWHDhkJfZgAAAAC4kYo05K1atUr9+/fXunXrtGTJEl26dEktW7bUuXPnnMr17t1bR44csf7Gjx9vjUtPT1ebNm2UlpamtWvX6tNPP9W0adM0atQoq0xiYqLatGmje++9V/Hx8Ro0aJB69eqlH3/80Sozc+ZMDRkyRKNHj9bmzZtVp04dxcTE6NixYzd+RQAAAABAIXEYY0xRVyLT8ePHFRwcrFWrVqlJkyaSrlzJq1u3riZNmpTje3744Qc98MADOnz4sEJCQiRJH3zwgUaMGKHjx4/Lw8NDI0aM0IIFC7Rjxw7rfZ07d9bp06e1aNEiSVL9+vV155136t1335UkZWRkKCIiQs8884z+9a9/XbPuycnJCgwM1JkzZxQQEPB3VgOAGyRq+GdFXQVb2zShW1FXAQCAYqGos0GxuifvzJkzkqRSpUo5DZ8+fbrKlCmjmjVrauTIkTp//rw1Li4uTrVq1bICniTFxMQoOTlZO3futMo0b97caZoxMTGKi4uTJKWlpWnTpk1OZVxcXNS8eXOrzNVSU1OVnJzs9AcAAAAARc2tqCuQKSMjQ4MGDVKjRo1Us2ZNa/hjjz2myMhIhYeHa9u2bRoxYoQSEhL07bffSpKSkpKcAp4k63VSUlKeZZKTk3XhwgWdOnVK6enpOZbZs2dPjvUdN26cXnrppb+30AAAAABQyIpNyOvfv7927Nihn3/+2Wn4U089Zf2/Vq1aCgsLU7NmzXTgwAFVqFDhn66mZeTIkRoyZIj1Ojk5WREREUVWHwAAAACQiknIGzBggObPn6/Vq1erbNmyeZatX7++JGn//v2qUKGCQkNDs/WCefToUUlSaGio9W/msKxlAgIC5O3tLVdXV7m6uuZYJnMaV/P09JSnp2f+FxIAAAAA/gFFek+eMUYDBgzQnDlztHz5ct12223XfE98fLwkKSwsTJIUHR2t7du3O/WCuWTJEgUEBKh69epWmWXLljlNZ8mSJYqOjpYkeXh4KCoqyqlMRkaGli1bZpUBAAAAgJtBkV7J69+/v2bMmKHvvvtO/v7+1j10gYGB8vb21oEDBzRjxgy1bt1apUuX1rZt2zR48GA1adJEtWvXliS1bNlS1atX1xNPPKHx48crKSlJL7zwgvr3729daevTp4/effddPffcc3ryySe1fPlyff3111qwYIFVlyFDhig2NlZ33HGH7rrrLk2aNEnnzp1Tjx49/vkVAwAAAAAFVKQh7/3335d05TEJWU2dOlXdu3eXh4eHli5dagWuiIgIdejQQS+88IJV1tXVVfPnz1ffvn0VHR0tX19fxcbGauzYsVaZ2267TQsWLNDgwYP11ltvqWzZsvr4448VExNjlenUqZOOHz+uUaNGKSkpSXXr1tWiRYuydcYCAAAAAMVZsXpO3s2sqJ+FAeDaeE7ejcVz8gAAuKKos0Gxek4eAAAAAODvIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbKdKQN27cON15553y9/dXcHCw2rdvr4SEBKcyFy9eVP/+/VW6dGn5+fmpQ4cOOnr0qFOZQ4cOqU2bNvLx8VFwcLCGDx+uy5cvO5VZuXKlbr/9dnl6eqpixYqaNm1atvpMnjxZ5cqVk5eXl+rXr68NGzYU+jIDAAAAwI1UpCFv1apV6t+/v9atW6clS5bo0qVLatmypc6dO2eVGTx4sObNm6dZs2Zp1apVOnz4sB5++GFrfHp6utq0aaO0tDStXbtWn376qaZNm6ZRo0ZZZRITE9WmTRvde++9io+P16BBg9SrVy/9+OOPVpmZM2dqyJAhGj16tDZv3qw6deooJiZGx44d+2dWBgAAAAAUAocxxhR1JTIdP35cwcHBWrVqlZo0aaIzZ84oKChIM2bM0COPPCJJ2rNnj6pVq6a4uDg1aNBAP/zwgx544AEdPnxYISEhkqQPPvhAI0aM0PHjx+Xh4aERI0ZowYIF2rFjhzWvzp076/Tp01q0aJEkqX79+rrzzjv17rvvSpIyMjIUERGhZ555Rv/617+uWffk5GQFBgbqzJkzCggIKOxVA6AQRA3/rKirYGubJnQr6ioAAFAsFHU2KFb35J05c0aSVKpUKUnSpk2bdOnSJTVv3twqU7VqVd16662Ki4uTJMXFxalWrVpWwJOkmJgYJScna+fOnVaZrNPILJM5jbS0NG3atMmpjIuLi5o3b26VuVpqaqqSk5Od/gAAAACgqBWbkJeRkaFBgwapUaNGqlmzpiQpKSlJHh4eKlGihFPZkJAQJSUlWWWyBrzM8Znj8iqTnJysCxcu6K+//lJ6enqOZTKncbVx48YpMDDQ+ouIiCjYggMAAABAISo2Ia9///7asWOHvvrqq6KuSr6MHDlSZ86csf5+//33oq4SAAAAAMitqCsgSQMGDND8+fO1evVqlS1b1hoeGhqqtLQ0nT592ulq3tGjRxUaGmqVuboXzMzeN7OWubpHzqNHjyogIEDe3t5ydXWVq6trjmUyp3E1T09PeXp6FmyBAQAAAOAGKdIrecYYDRgwQHPmzNHy5ct12223OY2PioqSu7u7li1bZg1LSEjQoUOHFB0dLUmKjo7W9u3bnXrBXLJkiQICAlS9enWrTNZpZJbJnIaHh4eioqKcymRkZGjZsmVWGQAAAAC4GRTplbz+/ftrxowZ+u677+Tv72/d/xYYGChvb28FBgaqZ8+eGjJkiEqVKqWAgAA988wzio6OVoMGDSRJLVu2VPXq1fXEE09o/PjxSkpK0gsvvKD+/ftbV9r69Omjd999V88995yefPJJLV++XF9//bUWLFhg1WXIkCGKjY3VHXfcobvuukuTJk3SuXPn1KNHj39+xQAAAABAARVpyHv//fclSffcc4/T8KlTp6p79+6SpIkTJ8rFxUUdOnRQamqqYmJi9N5771llXV1dNX/+fPXt21fR0dHy9fVVbGysxo4da5W57bbbtGDBAg0ePFhvvfWWypYtq48//lgxMTFWmU6dOun48eMaNWqUkpKSVLduXS1atChbZywAAAAAUJwVq+fk3cyK+lkYAK6N5+TdWDwnDwCAK4o6GxSb3jUBAAAAAH8fIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbKRIQ97q1avVtm1bhYeHy+FwaO7cuU7ju3fvLofD4fTXqlUrpzInT55U165dFRAQoBIlSqhnz55KSUlxKrNt2zY1btxYXl5eioiI0Pjx47PVZdasWapataq8vLxUq1YtLVy4sNCXFwAAAAButCINeefOnVOdOnU0efLkXMu0atVKR44csf6+/PJLp/Fdu3bVzp07tWTJEs2fP1+rV6/WU089ZY1PTk5Wy5YtFRkZqU2bNmnChAkaM2aMPvzwQ6vM2rVr1aVLF/Xs2VNbtmxR+/bt1b59e+3YsaPwFxoAAAAAbiCHMcYUdSUkyeFwaM6cOWrfvr01rHv37jp9+nS2K3yZdu/ererVq2vjxo264447JEmLFi1S69at9ccffyg8PFzvv/++nn/+eSUlJcnDw0OS9K9//Utz587Vnj17JEmdOnXSuXPnNH/+fGvaDRo0UN26dfXBBx/kq/7JyckKDAzUmTNnFBAQUIA1AOBGixr+WVFXwdY2TehW1FUAAKBYKOpsUOzvyVu5cqWCg4NVpUoV9e3bVydOnLDGxcXFqUSJElbAk6TmzZvLxcVF69evt8o0adLECniSFBMTo4SEBJ06dcoq07x5c6f5xsTEKC4uLtd6paamKjk52ekPAAAAAIpasQ55rVq10meffaZly5bptdde06pVq3T//fcrPT1dkpSUlKTg4GCn97i5ualUqVJKSkqyyoSEhDiVyXx9rTKZ43Mybtw4BQYGWn8RERF/b2EBAAAAoBC4FXUF8tK5c2fr/7Vq1VLt2rVVoUIFrVy5Us2aNSvCmkkjR47UkCFDrNfJyckEPQAAAABFrlhfybta+fLlVaZMGe3fv1+SFBoaqmPHjjmVuXz5sk6ePKnQ0FCrzNGjR53KZL6+VpnM8Tnx9PRUQECA0x8AAAAAFLWbKuT98ccfOnHihMLCwiRJ0dHROn36tDZt2mSVWb58uTIyMlS/fn2rzOrVq3Xp0iWrzJIlS1SlShWVLFnSKrNs2TKneS1ZskTR0dE3epEAAAAAoFAVachLSUlRfHy84uPjJUmJiYmKj4/XoUOHlJKSouHDh2vdunU6ePCgli1bpnbt2qlixYqKiYmRJFWrVk2tWrVS7969tWHDBq1Zs0YDBgxQ586dFR4eLkl67LHH5OHhoZ49e2rnzp2aOXOm3nrrLaefWg4cOFCLFi3SG2+8oT179mjMmDH65ZdfNGDAgH98nQAAAADA31GkIe+XX35RvXr1VK9ePUnSkCFDVK9ePY0aNUqurq7atm2bHnzwQVWuXFk9e/ZUVFSUfvrpJ3l6elrTmD59uqpWrapmzZqpdevWuvvuu52egRcYGKjFixcrMTFRUVFRGjp0qEaNGuX0LL2GDRtqxowZ+vDDD1WnTh198803mjt3rmrWrPnPrQwAAAAAKAQFek7efffdp2+//VYlSpRwGp6cnKz27dtr+fLlhVW/m0ZRPwsDwLXxnLwbi+fkAQBwRVFngwJdyVu5cqXS0tKyDb948aJ++umnv10pAAAAAEDBXNcjFLZt22b9f9euXU7PkUtPT9eiRYt0yy23FF7tAAAAAADX5bpCXt26deVwOORwOHTfffdlG+/t7a133nmn0CoHAAAAALg+1xXyEhMTZYxR+fLltWHDBgUFBVnjPDw8FBwcLFdX10KvJAAAAAAgf64r5EVGRkqSMjIybkhlAAAAAAB/z3WFvKz27dunFStW6NixY9lC36hRo/52xQAAAAAA169AIe+jjz5S3759VaZMGYWGhsrhcFjjHA4HIQ8AAAAAikiBQt5//vMfvfLKKxoxYkRh1wcAAAAA8DcU6Dl5p06d0qOPPlrYdQEAAAAA/E0FCnmPPvqoFi9eXNh1AQAAAAD8TQX6uWbFihX14osvat26dapVq5bc3d2dxj/77LOFUjkAAAAAwPUpUMj78MMP5efnp1WrVmnVqlVO4xwOByEPAAAAAIpIgUJeYmJiYdcDAAAAAFAICnRPHgAAAACgeCrQlbwnn3wyz/FTpkwpUGUAAAAAAH9PgULeqVOnnF5funRJO3bs0OnTp3XfffcVSsUAAAAAANevQCFvzpw52YZlZGSob9++qlChwt+uFAAAAACgYArtnjwXFxcNGTJEEydOLKxJAgAAAACuU6F2vHLgwAFdvny5MCcJAAAAALgOBfq55pAhQ5xeG2N05MgRLViwQLGxsYVSMQAAAADA9StQyNuyZYvTaxcXFwUFBemNN964Zs+bAAAAAIAbp0Ahb8WKFYVdDwAAAABAIShQyMt0/PhxJSQkSJKqVKmioKCgQqkUAAAAAKBgCtTxyrlz5/Tkk08qLCxMTZo0UZMmTRQeHq6ePXvq/PnzhV1HAAAAAEA+FSjkDRkyRKtWrdK8efN0+vRpnT59Wt99951WrVqloUOHFnYdAQAAAAD5VKCfa86ePVvffPON7rnnHmtY69at5e3trY4dO+r9998vrPoBAAAAAK5Dga7knT9/XiEhIdmGBwcH83NNAAAAAChCBQp50dHRGj16tC5evGgNu3Dhgl566SVFR0cXWuUAAAAAANenQD/XnDRpklq1aqWyZcuqTp06kqStW7fK09NTixcvLtQKAgAAAADyr0Ahr1atWtq3b5+mT5+uPXv2SJK6dOmirl27ytvbu1ArCAAAAADIvwKFvHHjxikkJES9e/d2Gj5lyhQdP35cI0aMKJTKAQAAAACuT4Huyfvf//6nqlWrZhteo0YNffDBB3+7UgAAAACAgilQyEtKSlJYWFi24UFBQTpy5MjfrhQAAAAAoGAKFPIiIiK0Zs2abMPXrFmj8PDwv10pAAAAAEDBFOievN69e2vQoEG6dOmS7rvvPknSsmXL9Nxzz2no0KGFWkEAAAAAQP4VKOQNHz5cJ06cUL9+/ZSWliZJ8vLy0ogRIzRy5MhCrSAAAAAAIP8KFPIcDodee+01vfjii9q9e7e8vb1VqVIleXp6Fnb9bCtq+GdFXQXb2zShW1FXAQAAAPjHFSjkZfLz89Odd95ZWHUBAAAAAPxNBep4BQAAAABQPBHyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwkSINeatXr1bbtm0VHh4uh8OhuXPnOo03xmjUqFEKCwuTt7e3mjdvrn379jmVOXnypLp27aqAgACVKFFCPXv2VEpKilOZbdu2qXHjxvLy8lJERITGjx+frS6zZs1S1apV5eXlpVq1amnhwoWFvrwAAAAAcKMVacg7d+6c6tSpo8mTJ+c4fvz48Xr77bf1wQcfaP369fL19VVMTIwuXrxolenatat27typJUuWaP78+Vq9erWeeuopa3xycrJatmypyMhIbdq0SRMmTNCYMWP04YcfWmXWrl2rLl26qGfPntqyZYvat2+v9u3ba8eOHTdu4QEAAADgBnAYY0xRV0KSHA6H5syZo/bt20u6chUvPDxcQ4cO1bBhwyRJZ86cUUhIiKZNm6bOnTtr9+7dql69ujZu3Kg77rhDkrRo0SK1bt1af/zxh8LDw/X+++/r+eefV1JSkjw8PCRJ//rXvzR37lzt2bNHktSpUyedO3dO8+fPt+rToEED1a1bVx988EG+6p+cnKzAwECdOXNGAQEB1ywfNfyzfK8bFMymCd2KugooZtjvbiz2OQAArrjebFDY3P7xOeZTYmKikpKS1Lx5c2tYYGCg6tevr7i4OHXu3FlxcXEqUaKEFfAkqXnz5nJxcdH69ev10EMPKS4uTk2aNLECniTFxMTotdde06lTp1SyZEnFxcVpyJAhTvOPiYnJ9vPRrFJTU5Wammq9Tk5OLoSlBgDAPvhi5cbjyxUAOSm2Ha8kJSVJkkJCQpyGh4SEWOOSkpIUHBzsNN7NzU2lSpVyKpPTNLLOI7cymeNzMm7cOAUGBlp/ERER17uIAAAAAFDoim3IK+5GjhypM2fOWH+///57UVcJAAAAAIpvyAsNDZUkHT161Gn40aNHrXGhoaE6duyY0/jLly/r5MmTTmVymkbWeeRWJnN8Tjw9PRUQEOD0BwAAAABFrdiGvNtuu02hoaFatmyZNSw5OVnr169XdHS0JCk6OlqnT5/Wpk2brDLLly9XRkaG6tevb5VZvXq1Ll26ZJVZsmSJqlSpopIlS1plss4ns0zmfAAAAADgZlGkIS8lJUXx8fGKj4+XdKWzlfj4eB06dEgOh0ODBg3Sf/7zH33//ffavn27unXrpvDwcKsHzmrVqqlVq1bq3bu3NmzYoDVr1mjAgAHq3LmzwsPDJUmPPfaYPDw81LNnT+3cuVMzZ87UW2+95dTRysCBA7Vo0SK98cYb2rNnj8aMGaNffvlFAwYM+KdXCQAAAAD8LUXau+Yvv/yie++913qdGbxiY2M1bdo0Pffcczp37pyeeuopnT59WnfffbcWLVokLy8v6z3Tp0/XgAED1KxZM7m4uKhDhw56++23rfGBgYFavHix+vfvr6ioKJUpU0ajRo1yepZew4YNNWPGDL3wwgv697//rUqVKmnu3LmqWbPmP7AWAAAAAKDwFJvn5N3seE5e8UO30rga+92NxT6Hq7HP3Xjsd0DxVNTPySu29+QBAAAAAK4fIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbMStqCsAAACA4iVq+GdFXQVb2zShW1FXATbHlTwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANuJW1BUAbjZRwz8r6irY3qYJ3Yq6CgAAADctruQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0Q8gAAAADARgh5AAAAAGAjhDwAAAAAsBFCHgAAAADYCCEPAAAAAGyEkAcAAAAANkLIAwAAAAAbIeQBAAAAgI0U65A3ZswYORwOp7+qVata4y9evKj+/furdOnS8vPzU4cOHXT06FGnaRw6dEht2rSRj4+PgoODNXz4cF2+fNmpzMqVK3X77bfL09NTFStW1LRp0/6JxQMAAACAQlesQ54k1ahRQ0eOHLH+fv75Z2vc4MGDNW/ePM2aNUurVq3S4cOH9fDDD1vj09PT1aZNG6WlpWnt2rX69NNPNW3aNI0aNcoqk5iYqDZt2ujee+9VfHy8Bg0apF69eunHH3/8R5cTAAAAAAqDW1FX4Frc3NwUGhqabfiZM2f0ySefaMaMGbrvvvskSVOnTlW1atW0bt06NWjQQIsXL9auXbu0dOlShYSEqG7dunr55Zc1YsQIjRkzRh4eHvrggw9022236Y033pAkVatWTT///LMmTpyomJiYf3RZAQAAAODvKvZX8vbt26fw8HCVL19eXbt21aFDhyRJmzZt0qVLl9S8eXOrbNWqVXXrrbcqLi5OkhQXF6datWopJCTEKhMTE6Pk5GTt3LnTKpN1GpllMqeRm9TUVCUnJzv9AQAAAEBRK9Yhr379+po2bZoWLVqk999/X4mJiWrcuLHOnj2rpKQkeXh4qESJEk7vCQkJUVJSkiQpKSnJKeBljs8cl1eZ5ORkXbhwIde6jRs3ToGBgdZfRETE311cAAAAAPjbivXPNe+//37r/7Vr11b9+vUVGRmpr7/+Wt7e3kVYM2nkyJEaMmSI9To5OZmgBwA3QNTwz4q6Cra3aUK3oq4CAKAQFesreVcrUaKEKleurP379ys0NFRpaWk6ffq0U5mjR49a9/CFhoZm620z8/W1ygQEBOQZJD09PRUQEOD0BwAAAABF7aYKeSkpKTpw4IDCwsIUFRUld3d3LVu2zBqfkJCgQ4cOKTo6WpIUHR2t7du369ixY1aZJUuWKCAgQNWrV7fKZJ1GZpnMaQAAAADAzaRYh7xhw4Zp1apVOnjwoNauXauHHnpIrq6u6tKliwIDA9WzZ08NGTJEK1as0KZNm9SjRw9FR0erQYMGkqSWLVuqevXqeuKJJ7R161b9+OOPeuGFF9S/f395enpKkvr06aNff/1Vzz33nPbs2aP33ntPX3/9tQYPHlyUiw4AAAAABVKs78n7448/1KVLF504cUJBQUG6++67tW7dOgUFBUmSJk6cKBcXF3Xo0EGpqamKiYnRe++9Z73f1dVV8+fPV9++fRUdHS1fX1/FxsZq7NixVpnbbrtNCxYs0ODBg/XWW2+pbNmy+vjjj3l8AgAAAICbUrEOeV999VWe4728vDR58mRNnjw51zKRkZFauHBhntO55557tGXLlgLVEQAAAACKk2L9c00AAAAAwPUh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ8AAAAAbISQBwAAAAA2QsgDAAAAABsh5AEAAACAjRDyAAAAAMBGCHkAAAAAYCNuRV0BAAAAAIUjavhnRV0FW9s0oVtRVyFfuJIHAAAAADZCyAMAAAAAGyHkAQAAAICNEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwAAAABshJAHAAAAADZCyAMAAAAAGyHkAQAAAICNEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwAAAABshJAHAAAAADZCyAMAAAAAGyHkAQAAAICNEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwAAAABshJAHAAAAADZCyAMAAAAAGyHkAQAAAICNEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwAAAABshJAHAAAAADZCyAMAAAAAGyHkAQAAAICNEPIAAAAAwEYIeQAAAABgI4S8q0yePFnlypWTl5eX6tevrw0bNhR1lQAAAAAg3wh5WcycOVNDhgzR6NGjtXnzZtWpU0cxMTE6duxYUVcNAAAAAPKFkJfFm2++qd69e6tHjx6qXr26PvjgA/n4+GjKlClFXTUAAAAAyBe3oq5AcZGWlqZNmzZp5MiR1jAXFxc1b95ccXFx2cqnpqYqNTXVen3mzBlJUnJycr7ml5564W/WGNeS37a4XrTdjUfb3Zxot5sXbXfzou1uTjeq3STa7kbLb9tlljPG3Mjq5MphimrOxczhw4d1yy23aO3atYqOjraGP/fcc1q1apXWr1/vVH7MmDF66aWX/ulqAgAAALhJ/P777ypbtuw/Pl+u5BXQyJEjNWTIEOt1RkaGTp48qdKlS8vhcBRhzW6M5ORkRURE6Pfff1dAQEBRVwf5RLvdvGi7mxdtd3Oi3W5etN3Ny85tZ4zR2bNnFR4eXiTzJ+T9/8qUKSNXV1cdPXrUafjRo0cVGhqarbynp6c8PT2dhpUoUeJGVrFYCAgIsN1O+P8C2u3mRdvdvGi7mxPtdvOi7W5edm27wMDAIps3Ha/8/zw8PBQVFaVly5ZZwzIyMrRs2TKnn28CAAAAQHHGlbwshgwZotjYWN1xxx266667NGnSJJ07d049evQo6qoBAAAAQL4Q8rLo1KmTjh8/rlGjRikpKUl169bVokWLFBISUtRVK3Kenp4aPXp0tp+oonij3W5etN3Ni7a7OdFuNy/a7uZF29049K4JAAAAADbCPXkAAAAAYCOEPAAAAACwEUIeAAAAANgIIQ+WlStXyuFw6PTp00VdFSdjxoxR3bp1813+ww8/VEREhFxcXDRp0qR8vz+v5T948KAcDofi4+PzXY8b4XrXRX45HA7NnTtXUvFY1nLlymnSpEn5Knv+/Hl16NBBAQEBVvvl9P7r3b5v1LouqnnmZ53mNv977rlHgwYNytd8jDF66qmnVKpUKWs7up7352batGn5ehbpjdh2rldRbDvX6+rlzHoM+LsKcjzJuo1cq61z28aefPLJv3Xsyu82dqOnO3fuXFWsWFGurq4aNGhQodTL4XBY7VKQY3xB63CjPk+6d++u9u3bW68L4xjzd/xT5yl5KYrP7ht13nh1exbGcfnvuHp7u5YxY8YoJCTE2ueu9/05KdC6Nrim2NhYIynbX0xMTL6n0bRpUzNw4MAbV8lCsGLFCiPJnDp1qsjqIMnMmTPHadjZs2dN586djSTz9NNPZ3tPv379jCQTGxtrzpw5Y9zd3c0777xjDh8+bM6dO2fOnj1r/vrrr2vOO6/lT0xMNJLMli1bCrhkzkaPHm3q1Knzj73vWrKu98uXL5sjR46YS5cuFfp8rjZ16lQTGBho7WOZ7Xvs2DFz7tw5Y4xz++bkvffeM0FBQWb79u3myJEjJiMjw+n9ma53+/4767qw2jdzvYwbN86p3Jw5c8z1Hr6vXic57WuZ87t6HZ04ccIkJyfnaz4LFy407u7uZs2aNdZ2dD3vz03mtpLb60w5tX1u8rvtXK8btZ8WpsjISDNx4kTr9ZEjR8zFixev+b5jx46ZPn36mIiICOPh4WFCQkJMy5Ytzc8//2yVyet4ktt+mHUbya1tMy1cuNA4HA7TuHFjp21s+/btf+s4fa355sfV69UYY86fP2+OHj2a72kEBwebESNGmD///NMkJydf8/35bZPMdinIMT4/6yY2Nta0a9fOaVhhf3bmNq/COMbkV27nKVnPM/I6b8zvecr1njfeqHWdlxt13nj1shfGcTk/cluHp0+fzvcy7tq1y9pGMo+r1/P+3BRkXfMIhXxq1aqVpk6d6jSssLt7NcYoPT1dbm40S1Z+fn7y9PRURESEvvrqK02cOFHe3t6SpIsXL2rGjBm69dZbJUmHDh3SpUuX1KZNG4WFhTlN45+QlpYmDw+Pf2ReN4qrq6tCQ0P/8flmbd+goCBJ2ds3JwcOHFC1atVUs2ZNa1jm+wtbUbSvl5eXXnvtNT399NMqWbJkgafzd9ZJqVKl8l32wIEDCgsLU8OGDSVdOa4FBAT8Y8e161nOf3Lb+SdcunRJ7u7uBXpvfvf5Dh06KC0tTZ9++qnKly+vo0ePatmyZTpx4kSO5fN7PLnebczHx0elSpWypl2qVCklJyfnexr/JG9vb+sz61pSUlJ07NgxxcTEKDw83GkaublRbXIzuZ7t50bw8/PLdp6R23ljYZ6n/L9y3ljUx+XAwMB8lz1w4IAkqV27dnI4HJIKPy/k29+Klf+PyOnbqaxWrFhh3N3dzerVq61hr732mgkKCjJJSUk5fqOTmJhopfKFCxea22+/3bi7u5sVK1aY9PR089///teUK1fOeHl5mdq1a5tZs2Y5zU+SWbRokalbt67x8vIy9957rzl69KhZuHChqVq1qvH39zddunRx+uYjv9M9deqUSUlJMf7+/k7jjblyBcHHxyfXb8xmzZplatasaby8vEypUqVMs2bNTEpKijHGmA0bNpjmzZub0qVLm4CAANOkSROzadMm672RkZFO6ygyMtIYc+Vb8ZIlS5p27dqZmjVrmn//+9/mzjvvND4+Psbb29v4+PiYFi1amEaNGmVbz6VLlzaurq7Gx8fHbNiwwZrXRx99ZMqWLWscDodxOBymUqVKZurUqdbyr1+/3tStW9e4u7sbPz8/4+HhYSSZzp07W8uTWeexY8eaJ554wvj7+1tXm5577jlTqVIl4+3tbW677TbzwgsvmLS0NGOMseaT9W/q1KnGGGNOnTplevbsacqUKWP8/f3Nvffea+Lj4635Zb1CsGrVKuPm5maOHDni1AYDBw40d999d47tY4wxe/fuNY0bNzaenp6mWrVqZvHixU7fTF79TdbJkyfNY489ZsqUKWO8vLxMxYoVzZQpU4wxxqSmppr+/fub0NBQ4+npaW699Vbz3//+15rXG2+8YWrWrGl8fHxM2bJlTd++fc3Zs2eNMf+3vWX9Cw4ONl988YX1bfj06dNN7dq1TZUqVYyPj4/x8PAwYWFhpl+/fuaZZ54x7u7uTu9v2rSp1S69e/c2lSpVMl5eXubuu+82jRs3NpKMn5+ftV5/+uknc/fddxsvLy8TGBhovL29ja+vr3nyySfNiBEjjLu7e5G2r8PhMC1atDBVq1Y1w4cPt9q3WrVq2a7kZS6Lm5ubcXV1NS4uLiYkJMQ888wz1jp57LHHTFRUlHE4HNn2tcx2z/rn7u5uOnXqZBo1amR9q5qenm4efPBB4+bmZiQZNzc3U79+fWNM9m+vPTw8jLu7u6lTp47Tt7IXL140gwYNMj4+PkaScTgcJiQkxGnb6dy5s9W+3t7eJjo62gQEBOS67YwePdpazswrKRkZGWb06NHWFY6wsDBrfTRt2jTXbSfrlZhrtZkxxowbN84EBwcbPz8/a9vJ60peZv2XLl1qoqKirOXbs2ePU7n33nvPlC9f3ri7u5vKlSubzz77zGm8JPPee++Ztm3bGh8fH2sdXO3o0aPmgQceMF5eXqZcuXJO+1jWaWUeA3Lbr0+dOmVdVc9pnRpjzGeffWYkGS8vLxMSEmLatm1rHU9y2sZCQ0PNtGnTjCTTp08fY8yVfcjb29uEhYVZx+iQkBBz+PDhXK+Q1KlTx/To0cOa14ULF0yJEiVMQECA8fHxMXfddZdZsWKF2bJli5Fk9u3bZ6ZOnWoiIiKMt7e3ad++vXn99deNr6+v9fkSGBhoGjZsaA4ePGiMMWb//v3mwQcfNMHBwcbX19fccccdZsmSJdayX71NZe6jV18Fi4+PN/fcc4/x8/Mz/v7+5vbbbzcbN27McbtesWJFjlfR5s6da+rVq2d9NnXv3t3pytzVx/nM6c2ZMyfHqxXfffedqVixovH09DT33HOP1SaZVw0y67Bo0SJTtWpV4+vra2JiYszhw4eNMVeOXTnVPeu8MjIyTIUKFcyECROcliVrm+Tk8uXLZvDgwSYwMNCUKlXKDB8+3HTr1s3pvOzqKz+TJ0+2lic4ONh06NDBGnejzlOy7vMxMTGmRIkS2bajnD4fEhMTzejRo01YWFie541jx451Om+sUqWKcTgcxt3d3dx+++2ma9euRpLx9PS8qc4bU1JSzBNPPGF8fX1NaGioef3117O1Z36P69dq+x9++ME0atTI2pbatGlj9u/fb42/ep1nfi5cnQNy24Zy2g9yev+11qsxxixYsMA6h7nnnnuczlHzi5CXD9cKecYYM3z4cBMZGWlOnz5tNm/ebDw8PMx3331njLlymTc6Otr07t3bHDlyxBw5csRcvnzZ2jlq165tFi9ebPbv329OnDhh/vOf/5iqVauaRYsWmQMHDpipU6caT09Ps3LlSmPM/+1UDRo0MD///LPZvHmzqVixomnatKlp2bKl2bx5s1m9erUpXbq0efXVV6065ne6mRtQ7969TevWrZ2W88EHHzTdunXLcR0cPnzYuLm5mTfffNMkJiaabdu2mcmTJ1sn9cuWLTOff/652b17t9m1a5fp2bOnCQkJsXb8Y8eOWSfER44cMceOHTPGOIe8CRMmGFdXVzNs2DCzf/9+06BBA9OlSxfTsmVL8/jjj5ulS5caSaZTp04mNDTUzJs3z/Tr18+ULFnSlCxZ0pw4ccJ88cUXJjg42Li7u5uePXuat99+2/j6+pqAgAAjyfz+++8mKCjItG3b1nh7e5tevXqZiIgII8lUrVrVdO/e3VrmyMhIExAQYF5//XWzf/9+62Dx8ssvmzVr1pjExETz/fffm5CQEPPaa68ZY678dGfo0KGmRo0a1vZw/vx5Y4wxzZs3N23btjUbN240e/fuNUOHDjWlS5c2J06csNZF1g+SypUrm/Hjx1uv09LSTJkyZawQdrX09HRTs2ZN06xZMxMfH29WrVpl6tWrl2fI69+/v6lbt67ZuHGjSUxMNEuWLDHff/+9McaYCRMmmIiICLN69Wpz8OBB89NPP5kZM2ZY85s4caJZvny5SUxMNMuWLTNVqlQxffv2NcZcOZGcNGmSCQgIMB07djQxMTFm3LhxplmzZtYBvVmzZqZ79+7Gzc3NNG/e3Pz2229m/fr15r777jPh4eFm5syZ5pFHHjFBQUGmRIkS1vq/5ZZbjJubmxkyZIjZs2ePqVGjhvH09DSSzC+//GKGDh1qSpQoYXx9fc3EiRPNpEmTjLu7u7n11lvNQw89ZJ5//nnj7+9v3N3di7R9AwICTI0aNcy3335rvLy8zK+//mrKlCljBgwY4BTy9u/fb3x9fU337t2Nr6+vef31102NGjVMmzZtzIcffmhtq507dzYLFy40GzZsMJJMhQoVzH333WeOHTtmLl++bDp27GgkmZYtW5oVK1aYhQsXmtDQUHPrrbdaH7h9+/Y1ksyIESPM6tWrzZgxY4ybm5tZuXKlOX36tHWiXb16dfP111+b/fv3m4YNGzp9YPfq1ctERkaa4OBg8+WXX5p///vfxt3d3bz55pvGGGPWrVtnHA6Heeqpp8yyZctM//79jYuLi/Hw8Mi27WSu48zjTNaTgVmzZpmAgACzcOFCa9vJXB8nTpwwvXv3NtHR0ebIkSNWG1wdfq7VZjNnzjSenp7m448/Nnv27LG2nfyEvPr165uVK1eanTt3msaNG5uGDRtaZb799lvj7u5uJk+ebBISEswbb7xhXF1dzfLly60ymV+MTJkyxRw4cMD89ttvOc7v/vvvN3Xq1DFxcXHml19+MQ0bNjTe3t65hrzc9utLly4ZLy8v6/Pt6nVqjDGffPKJkWTef/99ExcXZx1ftmzZYi5fvmzee+89I8k8+eST5qeffjKffPKJueWWW5xC3qhRo4wkU7NmTfP999+b6dOnm5CQEPPYY4+Z06dPm+eff954e3ube++91/qpbePGjZ1CXq9evcytt95qypUrZ/bv328mTJhgPD09Tbdu3UyTJk3MunXrjIuLi3nttddMQkKCeeutt0xgYKCRZH2+7Nq1y0ybNs1ar/Hx8eaDDz4w27dvN3v37jUvvPCC8fLyssafOHHClC1b1owdO9baLo3JHvJq1KhhHn/8cbN7926zd+9e8/XXX5v4+HiTmppqEhISjCQze/Zsc+TIEZOamprt/atXrzYBAQFm2rRpJiEhwXh7e5uAgADzwgsvGGNyPs7nFfJ+/fVX4+7uboYNG2b27NljvvzyS6tNsoY8d3d307x5c7Nx40azadMmU61aNfPYY48ZY678XLFjx46mVatW1rKnpqZmm9crr7xiqlev7rR9Pvvss6ZJkyY5brvGXPnSvGTJkmb27NnWuYO/v3+uIW/jxo3G1dXVzJgxwxw8eNBs3rzZvPXWW8aYG3uekrnPX7p0ybi7u5uKFStm247Onz9vnac8/vjjpmzZsubEiRPmqaeeMg6Hwzpv/PDDD631vX79evPxxx+bkiVLmhEjRlj7RmBgoGnbtq1Zs2aNefzxx60vxebNm3fTnDcac+Uz5dZbbzVLly4127ZtMw888IDx9/fPNeTldVzPq+2NMeabb74xs2fPNvv27TNbtmwxbdu2NbVq1TLp6enGGGN9Ni5dutTpcyFrDshrGzp79qwVxrIeA67OEddar4cOHTKenp7WOcwXX3xhQkJCCHk3QmxsrHF1dTW+vr5Of6+88opVJjU11dStW9d07NjRVK9e3fTu3dtpGjn9tjpz55g7d6417OLFi8bHx8esXbvWqWzPnj1Nly5dnN63dOlSa/y4ceOMJHPgwAFr2NNPP23dN3g9083cgNavX29cXV2tb+qOHj1qnczlZNOmTUaS9a3ntaSnpxt/f38zb948a1jWE41MWUNe5gfgV199ZQ4ePGi8vLzM8ePHTbt27UxsbKz1jaC7u7uZPn269f7atWub8PBwM378eFOhQgXz4IMPOn3QvPzyyyY8PNxIMhMnTjSlS5c23bt3N0899ZQxxpj333/fSDJTpkwxLi4u5sKFC8aYKwee9u3bX3NZJ0yYYKKiopyW6eqTwJ9++skEBARkuy+mQoUK5n//+1+O73vttddMtWrVrNezZ882fn5+Tlcbs/rxxx+Nm5ub+fPPP61hP/zwQ54hr23btqZHjx45Tu+ZZ54x9913n8nIyMhz+TPNmjXLlC5d2nqd9Z68du3amWPHjhlPT09zyy23mBdffNF4eXmZl156yfj6+ponnnjCGHPlW7+s7Ttw4EDTuHFjq32NuRKOQkJCjDH/t16HDRvmtH37+/ubxo0bG2OMiY6ONv369TM//fST1b7169c37u7uRdq+UVFRxs/PzxhjTIMGDcx9991n/Pz8zJdffukU8nr27Gmeeuop88Ybb5jKlSubtLQ0p2UxJnt4kWQmTJhgJFknOJnfHh86dMgqN3z4cOsD9+LFi8bT0zPbt7JZjyP9+/fPdlzLevz77bffjKurq3nyySedtp1mzZqZkSNHGmOM6dKlS7YThYYNGxqHw2G9zu0eoazLmXV95GTgwIHWN7U5vT8/bZa57WRVv379fF/Jy7RgwQIjyWqvhg0bZvscefTRR53WiyQzaNCgXOdjjLGOmVl/ybB7927rWJd1WpnHgLz269jYWOPi4mK8vLxMw4YNzciRI83WrVudymSd1nfffWckmTVr1hhjrrTt1Scqzz//vFPIy9yGsl4xnTx5srVPT5w40fj6+mY7yc8MeQsXLjSurq5m06ZNxtXV1axfv94YY8y9995rvL29zbRp03Lcxtq3b28k5foZl5MaNWqYd955x3qd0z15V2+r/v7+Ztq0aTlOL/Nq6YoVK3J9f7NmzZyuen/zzTfG19fXSDINGzY0nTp1Mq6urk7H+bxC3ogRI0zNmjWd6pHZJllDniSnqx5Z28SY/N2T9+effzq1SeYXk7mtD2OMCQsLc/oy89KlS6Zs2bK5hrzZs2ebgICAHK8c3cjzlMx9/sSJE0aScXFxyfG8MfM8JSEhwTpvDAoKMqVKlbKmV6FCBVOtWjWn88aXX37ZVK9e3Ugyffv2NaVLlzYXLlywzu+GDx/utK5vhvPGs2fPGg8PD/P1119bw06cOGG8vb1zDXl5HdfzavucHD9+3Egy27dvN8bkfk9e1m37WttQTvfMZ31/ftbryJEjs30Zkhnwryfk0btmPt17772Kj493+uvTp4813sPDQ9OnT9fs2bN18eJFTZw4Md/TvuOOO6z/79+/X+fPn1eLFi2s33j7+fnps88+s37nm6l27drW/0NCQuTj46Py5cs7DTt27Nh1TzfTXXfdpRo1aujTTz+VJH3xxReKjIxUkyZNcixfp04dNWvWTLVq1dKjjz6qjz76SKdOnbLGHz16VL1791alSpUUGBiogIAApaSk6NChQ/leV5UrV1ZkZKS6du2qFi1aqEqVKrp06VK2cpcuXVKjRo2s1w6HQ3fddZe2b9+uAwcOaP78+dq7d6+1Hv7zn//o3LlzkqSEhATVrl1bO3bs0LRp0+Tn56chQ4ZIkvr166eMjAwlJiZa087afplmzpypRo0aKTQ0VH5+fnrhhReuuZxbt25VSkqKSpcu7dRGiYmJubZR9+7dtX//fq1bt07SlR7QOnbsKF9f3xzL7969WxEREU73ekRHR+dZr759++qrr75S3bp19dxzz2nt2rVO84+Pj1eVKlX07LPPavHixU7vXbp0qZo1a6ZbbrlF/v7+euKJJ3TixAmdP38+x3kFBQWpTZs2SklJ0YYNG9SmTRv16NFDGRkZmj17tnr37q0PP/wwW/u6uLjorrvu0u7duyVdaf/IyEin9frOO+9Ikm655Rb5+fnp7NmzWrt2rfz8/BQXF6dPPvlEMTExVvtmrpeibN+KFSvq3LlzWrdunV577TWtWLFCLVu2lJeXV7ZpT5s2TS+++KL27dsnT09P3XvvvcrIyNC+ffuscr///rvatm1r3eP4/PPPS1K2uvv7+1v/DwsLs/ax/fv3KzU1VRcvXlRAQIDc3d3l5eWV43Ekp/UmSdu3b1d6erpmzJih5cuXy9XVVe7u7lqxYoU1jd27d6tMmTJO287GjRtljMl128nJo48+qgsXLqh8+fLq3bu35syZo8uXL+f7/flps927d6t+/fpO77vWPpUp6zE8896czGP27t27nbZxSWrUqJG1jWfKbT1n2r17t9zc3BQVFWUNq1q1ap49Jea1X7/88ssKDw+Xn5+fUlNT9e233+r222/XtGnTJEmbNm2SJPXq1Uv+/v7q1KmTJOnIkSOSrmyDV7vrrrucXtesWVMOh0PNmzfXE088oenTp6tUqVLWurmW/fv3Kz093fqsatSokfz8/LRq1SpdunRJjz76aI7tds8998jd3V0xMTFq27at3nrrLave0pX75YYNG6Zq1aqpRIkS8vPz0+7du6/rM0yShgwZol69eql58+Z69dVXc93/c7N161aNHTvW2h5jY2Ot7bpZs2Zav3690tPTsx2Pc5OQkKA777zTadjVbSJJPj4+qlChgvU6LCws322SKTw8XG3atNGUKVMkSfPmzVNqaqoeffTRHMufOXNGR44ccWorNze3PLf7Fi1aKDIyUuXLl7e2n8zjxj9xnlKqVClVqFBBxhjdddddGjRokBYvXpzneePly5etz+Vz587pwIEDSkhI0OTJk53OUw4fPizpymdc7dq15eXlZZ3fvfvuu5Kkhg0b3jTnjQcOHFBaWppT+5YqVUpVqlTJdf3mdVzPq+0lad++ferSpYvKly+vgIAAlStXTlL2z8C8XGsbupb8rNe/87mSFSEvn3x9fVWxYkWnv6tv9M08+T158qROnjx5XdPOlJKSIklasGCBU6DctWuXvvnmG6f3Zb3B3uFwZLvh3uFwKCMj47qnm1WvXr2sD++pU6eqR48e1o2kV3N1ddWSJUv0ww8/qHr16nrnnXdUpUoVKxDFxsYqPj5eb731ltauXav4+HiVLl1aaWlp+VlNlsmTJyskJER//fWXUlNTVbly5XzvYJknq/Xq1VO7du2s9bBjxw6NHz/eqWxKSoqefvppxcfHa+bMmZKunNzv27fP6YPu6kAVFxenrl27qnXr1po/f762bNmi559//prLmZKSorCwsGxfJiQkJGj48OE5vic4OFht27bV1KlTdfToUf3www968skn87Uu8uv+++/Xb7/9psGDB+vw4cNq1qyZhg0bJkm6/fbblZiYqJdfflkXLlxQx44d9cgjj0i60p3zAw88oNq1a2v27NnatGmTJk+eLEl5rosnn3xSKSkp2rhxo5588klFRESoWbNmatCggby9vfXKK69IUo7hPieZ6zWz++WffvpJ8fHxKl++vHr06KH4+Hj5+/vr5Zdf1tatW4tV+3p5eSk0NFRTp061PvSOHz+e47Sffvppbd26Vdu2bdP777+vLl26qEyZMurdu7cuXbokY4w++OADBQQEaPr06ZKkkSNHSsq7PRwOh4wx1nwkacmSJZo2bZq6d++u4OBghYWF6ZNPPnF6X25fNKSkpMjV1VXx8fHavHmzJk6cqIcffli+vr7WFy2pqamaMWOG07bToUOHa9b1ahEREUpISNB7770nb29v9evXT02aNLnubed62ux6XH0Ml2Qds/Mrt/X8d+S1X0dERGjv3r2aMmWKGjZsqDNnzqh06dIaPXq0zp07p5iYGElXgszGjRv1v//9T5KuK1x7eXkpICBAX375pcLCwjRq1CgNHjzY2g6v5fz583J1dbWOOd7e3lq3bp2aNm2qjh07ysfHJ9f3+vj4KC4uTg0bNtTMmTNVuXJl60u0YcOGac6cOfrvf/9rHUdq1ap13Z9hY8aM0c6dO9WmTRstX75c1atX15w5c/L9/pSUFL300ktO2+SOHTu0b98+jRkzRoMGDZKfn59Gjx59XfW6lpzOMfLbJln16tVLX331lS5cuKCpU6eqU6dOebbJ9fL399fmzZudtp86dero9OnT/9h5yt13362mTZuqRYsWWr58uWJiYvTXX3/let544cIFpaenS/q/42zlypXVtWtXpzZ+7733JMmpo5XM8plfZH711Vc3zXljQeR1XM+r7SWpbdu2OnnypD766COtX79e69evl3R9nyvX2oaupaDrtSAIeYXkwIEDGjx4sD766CPVr19fsbGxTh/WHh4e1g6cl+rVq1u9L10dKiMiIgpcv4JO9/HHH9dvv/2mt99+W7t27VJsbGye83E4HGrUqJFeeuklbdmyRR4eHtaH15o1a/Tss8+qdevWqlGjhjw9PfXXX385vd/d3f2a66lVq1aSrnwY79ixQzVr1sz27bCHh4fWrFljvTbGaOPGjYqKilJ4eLh8fHy0Z88ep/WQuYNWqVJF27ZtU506dbRr1y5VrFjR+vbs1ltvVcWKFfPsYXHt2rWKjIzU888/rzvuuEOVKlXSb7/9lq1+Vy/n7bffrqSkJLm5uWVrozJlyuQ6v169emnmzJn68MMPVaFChWzf/mdVrVo1/f77707fTmeewOQlKChIsbGx+uKLLzRp0iR9+OGH1riAgAB16tRJH330kWbOnKnZs2fr5MmT2rRpkzIyMvTGG2+oQYMGqly5srUe81oPme2bnp5unTC6uroqIiJCb7/9tpYsWSJJVvCWrpwYb9y4UdWrV5d0ZTvK/GYuc71u27ZNklS+fHlVrFhRDRo0UGJioipWrKiaNWvq4MGD1vr28PDIdb380+0bGRlptW9kZKTWrFmjuLi4bNPO3FZr1qypp59+Wp999pl+/vlnrV+/Xtu3b9elS5d0/vx5vfrqq2rcuLHc3d2zfTni6upqrfucZB5H/vzzT8XGxuqjjz7S7t27dfjwYe3duzfXZciqXr16Sk9P17Fjx1SvXj0NHDhQM2fO1KxZs7RgwQKdPHlSpUuXVnp6utO2s3Pnzmuu45x4e3urbdu2evvtt7Vy5UrFxcVp+/bt+aprftqsWrVq1olCpvzsU9dSrVo1p2OYdOUYmrmN51fVqlV1+fJl6wqbdOXKzbWetZTbfi1lX6fHjh3TmTNntGfPHqtHx+rVq6tq1arZenjM/OY8a9tt3Lgxxzo0b95c48eP17Zt27JdMXI4HLm2f9WqVa1tLPOK4uLFi/XTTz9pwIABkvJut3r16mnkyJFau3atatasqRkzZki6sv67d++uhx56SLVq1VJoaKgOHjzoNI38bpeVK1fW4MGDtXjxYj388MPZemHMy+23366EhIRs22TFihXl4uKiatWq6fz58zp79my+plelShX98ssvTsNya5O85HfZW7duLV9fX73//vtatGhRnl9MBgYGKiwszKmtrt6ec+Lm5ua0/Rw8eFDLly+X9M+cp2TWPaftKFPml6cfffSRbrnlFv3+++/KyMhQSEiIwsPDrdCStX0zr/hnnqdcvHjROi7//PPPkv7vPOVmOG+sUKGC3N3dndr31KlT1/w8yeu4nlvbnzhxQgkJCXrhhRfUrFkzVatWLdtnYOa53bXaN69t6Frys16rVaumDRs2OL2vIJ8r9u5ztRClpqYqKSnJaZibm5vKlCmj9PR0Pf7444qJiVGPHj3UqlUr1apVS2+88Yb1bW+5cuW0fv16HTx4UH5+frl29+vv769hw4Zp8ODBysjI0N13360zZ85ozZo1CggIuGbIyk1Bp1uyZEk9/PDDGj58uFq2bKmyZcvmOo/169dr2bJlatmypYKDg7V+/XodP35c1apVkyRVqlRJn3/+ue644w4lJydr+PDh2bqFLleunJYtW6ZGjRrJ09MzW5fxiYmJ+vDDD/Xpp58qNDRUy5Yt0759+3TLLbc4levatauGDx+uUqVK6fjx41YI7Nmzp0qUKKEBAwbo8uXL6tmzp9q1a6cFCxboyy+/lCQ98sgjeuWVV5SSkqI1a9aobdu22rp1qyRpxYoV+vjjj62fReSkUqVKOnTokL766ivdeeedWrBgQbadv1y5ckpMTFR8fLzKli0rf39/NW/eXNHR0Wrfvr3Gjx9vhaIFCxbooYceyvXnKTExMQoICNB//vMfjR07Ntd6SVdOmipXrqzY2FhNmDBBycnJ1k/2cjNq1ChFRUWpRo0aSk1N1fz58602ffPNNxUWFqZ69erJxcVFs2bNUmhoqEqUKKGKFSvq0qVLeuedd9S2bVutWbNGH3zwQbb1kJKSoiNHjsjV1VXnz5+Xj4+PwsPD1a9fP7m6umratGn67bffZIzRr7/+qm+//Vaurq569913FRUVpRMnTmjv3r1KS0tTz549JV3Z3pOSkjR8+HD17NlTFSpU0EcffSTpys8ydu3aJR8fH/38888aMGCA2rVrp1GjRsnV1VUnT55UhQoVsoWKomrf4OBg/fnnn1b77ty5U2+//bZTmREjRqhBgwZq1qyZ7r77bt111136448/9PHHH8vb21uRkZFyc3OTq6ur3nnnHfXp00dBQUHWT2oyu57P7CZ6/vz5at26dbb909/fX+3atVPfvn2VmJioe+65Rz/88IPS09O1Z8+ePLejTJnfTrdr106xsbFq06aNTp8+rddee00lS5ZUiRIl9NRTT+nnn3/WQw89pGeffVaffvqpduzYkW0dp6SkaNmyZapTp458fHyyXQ2YNm2a0tPTVb9+ffn4+OiLL76w1kd+5KfNBg4cqO7du+uOO+5Qo0aNNH36dO3cudPpJ1AFMXz4cHXs2FH16tVT8+bNNW/ePH377bdaunTpdU2nSpUqatWqlZ5++mm9//77cnNz06BBg/Lskj+3/To9PV3VqlVTo0aNdP/996tMmTLWtvjAAw/o1ltvlYeHh9LS0pSUlKTvv//eurqQqV+/fvriiy/UrVs3vfjii9q7d6/1rX+m+Ph4paWlKT4+XiVLltTChQuzXTFyc3PTtm3blJCQoNKlSzt9qZr5k/5u3brpjTfeUPv27fWvf/1LJUuWtILqs88+q0aNGun1119Xu3bt9OOPP2rBggVKS0tTXFycwsPDlZCQoH379qlbt26Sruz73377rdq2bSuHw6EXX3wx25XXcuXKafXq1ercubM8PT2zfYFz4cIFDR8+XI888ohuu+02/fHHH9q4caN1pTo/Ro0aZa3v5s2ba+jQoYqKilJaWpr+/e9/69SpU3I4HPL29tbWrVuv+WiJp59+Wm+++aZGjBihnj17Kj4+3mqT67kCU65cOf34449Wm+TW7byrq6u6d++ukSNHqlKlStf8GdrAgQP16quvqlKlSqpatarefPPNPL+kmD9/vn799Vc1adLE2n4yMjJUpUqVf+w8ZdOmTfLz89PGjRutn162a9fOKTAOHjzYOm/csWOH3nrrLeu88aWXXlKfPn30/fff66GHHpKbm5v27dtnhdvM85TevXtr5MiRat++vT777DNJssLizXDe6Ofnp549e2r48OEqXbq0goOD9fzzz8vFJfdrUHkd1/Nq+5IlS6p06dL68MMPFRYWpkOHDulf//qX07SDg4Pl7e2tRYsWqWzZsvLy8sq2HV9rGyqM9dqnTx9rW+jVq5c2bdqU7TiZL/m+e+//Ybl12VylShVjjDEvvfSSCQsLc3qQ5ezZs42Hh4d143hCQoJp0KCB8fb2NpLzIxSuvokyIyPDTJo0yVSpUsW4u7uboKAgExMTY1atWmWMyfmBiDl1QnB1Jw4Fma4xV3qbkuR0Y2xOdu3aZWJiYkxQUJDx9PQ0lStXdrohffPmzeaOO+4wXl5eplKlSmbWrFnZblL//vvvTcWKFY2bm1uOj1BISkoy7du3N2FhYcbDw8NERkaaUaNGmQcffNCp45U9e/aYZ555xpQpUybHRyhMnz7d3HbbbVZbBgQEWDf7nzp1ysTFxZk6deoYNzc34+/vb7y8vIwkU6lSJacOd3K6yd6YK51VlC5d2vj5+ZlOnTqZiRMnOrXPxYsXTYcOHUyJEiWsnrqMMSY5Odk888wzJjw83Li7u5uIiAjTtWtXqyOM3B6y/OKLLzrd7JyXhIQEc/fddxsPDw9TuXJls2jRojw7Xnn55ZdNtWrVjLe3tylVqpRp166d+fXXX40xV3oAq1u3rtU7abNmzczmzZuteb355psmLCzMeHt7m5iYGKt79azbWJ8+fayeL3PqBn/OnDmmZMmSxt3d3fj6+poGDRqYhQsXOrVvQECAU/tGRkaaXr16Wd0oN2zY0DRr1sxIV7r8z1yv8+bNMy1atLAek+Hq6mo8PDxMbGysee6554y7u3uRtm/mzdpZ2zcxMdHqNj2rDRs2mDp16hgXFxfrpv+IiAjrRvvIyEjzxBNPmHLlyhlPT09TpUoVExYWZiSZsLAwa/6hoaEmNDTUOBwOExsbayZOnGg8PT2tm+BXr15tKlSoYM3H1dXV1KlTxzqOZN2PMl3d8VRaWpp54IEHrOVwOBwmKCjIzJw50yrTqVMnax5BQUE5dtjRp08fU7p06Ty3nfr165uAgABr28na8cC1Ol7JT5sZc6XHwDJlyhg/Pz9r28lPxytZlyXz2JWYmGgNy88jFK7uACInR44cMW3atLEeh/DZZ5/l+QiF3Pbrixcvmocffth69IV05VEJXbt2tXqQnTFjhrWfRUdHm48++sjqLCvTY489ZlxdXY105REKmR1b9e/f3xhzpdMBV1dXU7JkSePt7W1q165tdZxkzJWOV8qWLWvtu1L2RyikpaWZUaNGmXLlylmP+6hRo4bZtm2bVY9PPvnElC1b1nh7e5u2bdua0aNHGzc3t2yfL5k97yUmJlqdt0RERJh3330327YdFxdnateubR3TjHH+fE5NTTWdO3e2un8PDw83AwYMsDrcyU/HK8YYs2jRItOwYUOrt9OsjxTK7MU4OjraOs5ntld+H6GQ2SaZ9cqpDld3MHHs2DGnNrn6EQpZHThwwEhy6lAlN5cuXTIDBw40AQEBpkSJEmbIkCF5PkLhp59+Mk2bNnXafjKPLTfyPCVzn09KSrJ65M7pvDFzXw8ODrbOG0ePHm0iIyOdzhtff/11p33trrvuMmPHjs12nuLh4eG0/bu5ud00543GXOl85fHHHzc+Pj4mJCTEjB8/Ps9HKOR1XM+r7Y0xZsmSJaZatWrWYyZWrlyZ7Tj60UcfmYiICOPi4pLjIxSutQ1dq+OV/KxXY4yZN2+etU82btzYTJky5bo7XnEYU4AfVOP/KZ9//rl1P9bN/qBvu+rZs6eOHz+u77//vqirghuA9oWdvfLKK/rggw9y7JSlMPz0009q1qyZfv/9d4WEhNyQedgNbYK/g/PG4oGfayJX58+f15EjR/Tqq6/q6aefZkcths6cOaPt27drxowZBAAbon1hR++9957uvPNOlS5dWmvWrNGECROse+UKU2pqqo4fP64xY8bo0UcfJUzkgTZBYeC8sXih4xXkavz48apatapCQ0OtXvhQvLRr104tW7ZUnz591KJFi6KuDgoZ7Qs72rdvn9q1a6fq1avr5Zdf1tChQzVmzJhCn8+XX36pyMhInT59OlvvyXBGm6AwcN5YvPBzTQAAAACwEa7kAQAAAICNEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwBw01m5cqUcDodOnz5dJPMfM2aM6tatm+/yH374oSIiIuTi4qJJkybdsHrdc889GjRo0A2bPgDg5kDIAwAUC927d1f79u0LdZofffSR6tSpIz8/P5UoUUL16tXTuHHjrmsaDodDc+fOdRo2bNgwLVu2LF/vT05O1oABAzRixAj9+eefeuqpp65r/jnJLeR+++23evnll//29AEANze3oq4AAAA3wpQpUzRo0CC9/fbbatq0qVJTU7Vt2zbt2LHjb0/bz89Pfn5++Sp76NAhXbp0SW3atFFYWNjfnndeSpUqdUOnDwC4OXAlDwBQ7KSmpurZZ59VcHCwvLy8dPfdd2vjxo3Zyq1Zs0a1a9eWl5eXGjRo4BTgvv/+e3Xs2FE9e/ZUxYoVVaNGDXXp0kWvvPKKVWbjxo1q0aKFypQpo8DAQDVt2lSbN2+2xpcrV06S9NBDD8nhcFivr/655sqVK3XXXXfJ19dXJUqUUKNGjfTbb79p2rRpqlWrliSpfPnycjgcOnjwoA4cOKB27dopJCREfn5+uvPOO7V06dJs62DEiBGKiIiQp6enKlasqE8++UQHDx7UvffeK0kqWbKkHA6HunfvLin7zzVPnTqlbt26qWTJkvLx8dH999+vffv2WeOnTZumEiVK6Mcff1S1atXk5+enVq1a6ciRI/lvLABAsUPIAwAUO88995xmz56tTz/9VJs3b1bFihUVExOjkydPOpUbPny43njjDW3cuFFBQUFq27atLl26JEkKDQ3VunXr9Ntvv+U6n7Nnzyo2NlY///yz1q1bp0qVKql169Y6e/asJFnBcurUqTpy5EiOQfPy5ctq3769mjZtqm3btikuLk5PPfWUHA6HOnXqZIW3DRs26MiRI4qIiFBKSopat26tZcuWacuWLWrVqpXatm2rQ4cOWdPt1q2bvvzyS7399tvavXu3/ve//8nPz08RERGaPXu2JCkhIUFHjhzRW2+9lePyde/eXb/88ou+//57xcXFyRij1q1bW+tIks6fP6/XX39dn3/+uVavXq1Dhw5p2LBh12wjAEAxZgAAKAZiY2NNu3btTEpKinF3dzfTp0+3xqWlpZnw8HAzfvx4Y4wxK1asMJLMV199ZZU5ceKE8fb2NjNnzjTGGHP48GHToEEDI8lUrlzZxMbGmpkzZ5r09PRc65Cenm78/f3NvHnzrGGSzJw5c5zKjR492tSpU8earySzcuXKHKe5ZcsWI8kkJibmufw1atQw77zzjjHGmISEBCPJLFmyJMeymct/6tQpp+FNmzY1AwcONMYYs3fvXiPJrFmzxhr/119/GW9vb/P1118bY4yZOnWqkWT2799vlZk8ebIJCQnJs64AgOKNK3kAgGLlwIEDunTpkho1amQNc3d311133aXdu3c7lY2Ojrb+X6pUKVWpUsUqExYWpri4OG3fvl0DBw7U5cuXFRsbq1atWikjI0OSdPToUfXu3VuVKlVSYGCgAgIClJKS4nRF7VpKlSql7t27KyYmRm3bttVbb711zZ87pqSkaNiwYapWrZpKlCghPz8/7d6925pvfHy8XF1d1bRp03zX42q7d++Wm5ub6tevbw0rXbq00zqSJB8fH1WoUMF6HRYWpmPHjhV4vgCAokfIAwDYWs2aNdWvXz998cUXWrJkiZYsWaJVq1ZJkmJjYxUfH6+33npLa9euVXx8vEqXLq20tLTrmsfUqVMVFxenhg0baubMmapcubLWrVuXa/lhw4Zpzpw5+u9//6uffvpJ8fHxqlWrljVfb2/vgi/wdXJ3d3d67XA4ZIz5x+YPACh8hDwAQLFSoUIFeXh4aM2aNdawS5cuaePGjapevbpT2axB6tSpU9q7d6+qVauW67Qz33/u3DlJVzpuefbZZ9W6dWvVqFFDnp6e+uuvv5ze4+7urvT09GvWu169eho5cqTWrl2rmjVrasaMGbmWXbNmjbp3766HHnpItWrVUmhoqA4ePGiNr1WrljIyMqwwejUPDw9JyrNe1apV0+XLl7V+/Xpr2IkTJ5SQkJBtPQIA7IWQBwAoVnx9fdW3b18NHz5cixYt0q5du9S7d2+dP39ePXv2dCo7duxYLVu2TDt27FD37t1VpkwZ61l7ffv21csvv6w1a9bot99+07p169StWzcFBQVZP/OsVKmSPv/8c+3evVvr169X165ds11FK1eunJYtW6akpCSdOnUqW30TExM1cuRIxcXF6bffftPixYu1b9++PMNmpUqV9O233yo+Pl5bt27VY489Zv2ENHOesbGxevLJJzV37lwlJiZq5cqV+vrrryVJkZGRcjgcmj9/vo4fP66UlJQc59GuXTv17t1bP//8s7Zu3arHH39ct9xyi9q1a5e/xgAA3JQIeQCAYiEjI0Nublce3/rqq6+qQ4cOeuKJJ3T77bdr//79+vHHH1WyZEmn97z66qsaOHCgoqKilJSUpHnz5llXuZo3b65169bp0UcfVeXKldWhQwd5eXlp2bJlKl26tCTpk08+0alTp3T77bfriSeesB7bkNUbb7yhJUuWKCIiQvXq1ctWbx8fH+3Zs0cdOnRQ5cqV9dRTT6l///56+umnc13WN998UyVLllTDhg3Vtm1bxcTE6Pbbb3cq8/777+uRRx5Rv379VLVqVfXu3du6AnnLLbfopZde0r/+9S+FhIRowIABOc5n6tSpioqK0gMPPKDo6GgZY7Rw4cJsP9EEANiLw/DDewBAMdCqVStVrFhR7777blFXBQCAmxpX8gAARerUqVOaP3++Vq5cqebNmxd1dQAAuOm5FXUFAAD/b3vyySe1ceNGDR06lHvFAAAoBPxcEwAAAABshJ9rAgAAAICNEPIAAAAAwEYIeQAAAABgI4Q8AAAAALARQh4AAAAA2AghDwAAAABshJAHAAAAADZCyAMAAAAAG/n/AGMEy8NU7U7KAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create the count plot\n",
    "ax = sns.countplot(x='JobSatisfaction', data=df, palette='viridis')\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('Job Satisfaction Distribution', fontsize=16)\n",
    "plt.xlabel('Job Satisfaction Level', fontsize=14)\n",
    "plt.ylabel('Count', fontsize=14)\n",
    "\n",
    "# Display counts on top of the bars\n",
    "for p in ax.patches:\n",
    "    ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                ha='center', va='baseline', fontsize=12, color='black', xytext=(0, 5), \n",
    "                textcoords='offset points')\n",
    "\n",
    "# Rotate x-axis labels if necessary\n",
    "plt.xticks(rotation=45, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7x73vib8Obvk"
   },
   "outputs": [],
   "source": [
    "# Select relevant features for prediction , which are relatade to job\n",
    "features = ['Hobby', 'OpenSource', 'Country', 'Student', 'Employment', 'FormalEducation',\n",
    "            'UndergradMajor', 'CompanySize', 'DevType', 'YearsCoding', 'YearsCodingProf',\n",
    "            'JobSatisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5ivNFXwbO2Vl"
   },
   "outputs": [],
   "source": [
    "# Filter the dataset with selected features\n",
    "df = df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5IC3YEsDObyf"
   },
   "outputs": [],
   "source": [
    "# Drop rows with missing target value\n",
    "df = df.dropna(subset=['JobSatisfaction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z7tM54s9Ob1I"
   },
   "outputs": [],
   "source": [
    "# Split features and target\n",
    "X = df.drop('JobSatisfaction', axis=1)\n",
    "y = df['JobSatisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mb_yUbbWOb37"
   },
   "outputs": [],
   "source": [
    "# Define categorical and numerical features\n",
    "categorical_features = X.select_dtypes(include=['object']).columns\n",
    "numerical_features = X.select_dtypes(include=['int64', 'float64']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5luiMUypOI3h"
   },
   "outputs": [],
   "source": [
    "# Preprocessing pipelines for numerical and categorical data\n",
    "numerical_pipeline = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wzSnaqicPyd8"
   },
   "outputs": [],
   "source": [
    "categorical_pipeline = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GN5N9QGrPyg-"
   },
   "outputs": [],
   "source": [
    "# Combine preprocessing pipelines\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numerical_pipeline, numerical_features),\n",
    "        ('cat', categorical_pipeline, categorical_features)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Hb2Osu0ZPykj"
   },
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5oOnOyFaQTsA"
   },
   "outputs": [],
   "source": [
    "# Create and fit the pipeline\n",
    "model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', RandomForestClassifier(random_state=42))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XOaOUYi1P2yA"
   },
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y6mT8lvLQbtg"
   },
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZCSpSAuDdNgi"
   },
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "yksyXWGZdR4_",
    "outputId": "3deaf8a9-8e7f-4d48-9562-60b99aa96e01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the model to a file\n",
    "joblib.dump(model, 'model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "fw6wBmNwQko4",
    "outputId": "486c5704-db44-44db-8d00-c6928a43810e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3393475750577367\n",
      "Confusion Matrix:\n",
      "[[   6   52    9  382    8   14   26]\n",
      " [   3  298   37 1950   28   56   87]\n",
      " [   7  113   25 1006   14   46   67]\n",
      " [  15  466   88 4182   61  118  275]\n",
      " [   6   85   19  818   26   38   51]\n",
      " [   5  104   24 1107   21   60   85]\n",
      " [  12  181   37 1548   32   53  105]]\n",
      "Classification Report:\n",
      "                                    precision    recall  f1-score   support\n",
      "\n",
      "            Extremely dissatisfied       0.11      0.01      0.02       497\n",
      "               Extremely satisfied       0.23      0.12      0.16      2459\n",
      "           Moderately dissatisfied       0.10      0.02      0.03      1278\n",
      "              Moderately satisfied       0.38      0.80      0.52      5205\n",
      "Neither satisfied nor dissatisfied       0.14      0.02      0.04      1043\n",
      "             Slightly dissatisfied       0.16      0.04      0.07      1406\n",
      "                Slightly satisfied       0.15      0.05      0.08      1968\n",
      "\n",
      "                          accuracy                           0.34     13856\n",
      "                         macro avg       0.18      0.15      0.13     13856\n",
      "                      weighted avg       0.24      0.34      0.25     13856\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#Adding more libraries required\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
    "\n",
    "\n",
    "\n",
    "# Printing accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# Printing classification report\n",
    "print('Classification Report:')\n",
    "report = classification_report(y_test, y_pred, output_dict=True)\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Converting classification report to a DataFrame for better readability\n",
    "report_df = pd.DataFrame(report).transpose()\n",
    "\n",
    "# Plotting confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.show()\n",
    "\n",
    "# If the model is a binary classifier, plot the ROC curve\n",
    "if len(set(y_test)) == 2:\n",
    "    fpr, tpr, _ = roc_curve(y_test, y_pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "huZqOC5mTaJm"
   },
   "outputs": [],
   "source": [
    "#Testing the Model\n",
    "def predict_job_satisfaction(user_input):\n",
    "    # Convert user input to DataFrame\n",
    "    input_df = pd.DataFrame([user_input])\n",
    "\n",
    "    # Ensure the input has the same columns as the training data\n",
    "    input_df = input_df[X.columns]\n",
    "\n",
    "    # Make prediction\n",
    "    prediction = model.predict(input_df)\n",
    "\n",
    "    return prediction[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "yHhyCWmNTi-Y"
   },
   "outputs": [],
   "source": [
    "# Example user input\n",
    "user_input_example = {\n",
    "    'Hobby': 'Yes',\n",
    "    'OpenSource': 'Yes',\n",
    "    'Country': 'United States',\n",
    "    'Student': 'No',\n",
    "    'Employment': 'Employed full-time',\n",
    "    'FormalEducation': 'Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
    "    'UndergradMajor': 'Computer science, computer engineering, or software engineering',\n",
    "    'CompanySize': '100 to 499 employees',\n",
    "    'DevType': 'Developer, back-end',\n",
    "    'YearsCoding': '6-8 years',\n",
    "    'YearsCodingProf': '3-5 years'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "vvGywGhnTlRM",
    "outputId": "f00c365e-1f2b-4c39-cee5-41a68383c64c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Job Satisfaction: Slightly satisfied\n"
     ]
    }
   ],
   "source": [
    "# Predict job satisfaction for the example input\n",
    "predicted_satisfaction = predict_job_satisfaction(user_input_example)\n",
    "print(f'Predicted Job Satisfaction: {predicted_satisfaction}')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}