ml-report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1a30920",
   "metadata": {},
   "source": [
    "## Machine Learning Model Tracking\n",
    "\n",
    "This is part of a series of tutorials to help you learn how to use Datapane. In this example we use Datapane to produce automated reports of a long-running process, such as an ML pipeline.\n",
    "\n",
    "# Conducting the Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45a6373a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.35.2.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the librarys\n",
    "import pandas as pd  # To work with dataset\n",
    "\n",
    "# from pandas_profiling import ProfileReport\n",
    "import numpy as np  # Math library\n",
    "import json\n",
    "import datapane as dp\n",
    "from datetime import datetime\n",
    "import seaborn as sns  # Graph library that use matplot in background\n",
    "import matplotlib.pyplot as plt  # to plot some parameters in seaborn\n",
    "import plotly.offline as py\n",
    "\n",
    "py.init_notebook_mode(connected=True)  # this code, allow us to work with offline plotly version\n",
    "import plotly.graph_objs as go  # it's like \"plt\" of matplot\n",
    "import plotly.tools as tls  # It's useful to get some tools of plotly\n",
    "import plotly.figure_factory as ff\n",
    "from plotly import tools\n",
    "import plotly.express as px\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score  # to split the data\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    confusion_matrix,\n",
    "    classification_report,\n",
    "    roc_curve,\n",
    "    auc,\n",
    ")  # To evaluate our model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Algorithmns models to be compared\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c899a0b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the data\n",
    "df_credit = pd.read_csv(\n",
    "    \"https://raw.githubusercontent.com/shoumikgoswami/ML-pipeline/main/data/german_credit_data.csv\", index_col=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1bcce42b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Age               1000 non-null   int64 \n",
      " 1   Sex               1000 non-null   object\n",
      " 2   Job               1000 non-null   int64 \n",
      " 3   Housing           1000 non-null   object\n",
      " 4   Saving accounts   817 non-null    object\n",
      " 5   Checking account  606 non-null    object\n",
      " 6   Credit amount     1000 non-null   int64 \n",
      " 7   Duration          1000 non-null   int64 \n",
      " 8   Purpose           1000 non-null   object\n",
      " 9   Risk              1000 non-null   object\n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 85.9+ KB\n",
      "None\n",
      "Age                  53\n",
      "Sex                   2\n",
      "Job                   4\n",
      "Housing               3\n",
      "Saving accounts       4\n",
      "Checking account      3\n",
      "Credit amount       921\n",
      "Duration             33\n",
      "Purpose               8\n",
      "Risk                  2\n",
      "dtype: int64\n",
      "   Age     Sex  Job Housing Saving accounts Checking account  Credit amount  \\\n",
      "0   67    male    2     own             NaN           little           1169   \n",
      "1   22  female    2     own          little         moderate           5951   \n",
      "2   49    male    1     own          little              NaN           2096   \n",
      "3   45    male    2    free          little           little           7882   \n",
      "4   53    male    2    free          little           little           4870   \n",
      "\n",
      "   Duration              Purpose  Risk  \n",
      "0         6             radio/TV  good  \n",
      "1        48             radio/TV   bad  \n",
      "2        12            education  good  \n",
      "3        42  furniture/equipment  good  \n",
      "4        24                  car   bad  \n"
     ]
    }
   ],
   "source": [
    "# Searching for Missings,type of data and also known the shape of data\n",
    "print(df_credit.info())\n",
    "\n",
    "# Looking unique values\n",
    "print(df_credit.nunique())\n",
    "# Looking the data\n",
    "print(df_credit.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "841db913",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ProfileReport' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# enable once ydata-profiling supports 3.11\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m profile \u001b[38;5;241m=\u001b[39m \u001b[43mProfileReport\u001b[49m(df_credit, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData Profile Report\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      3\u001b[0m json_data \u001b[38;5;241m=\u001b[39m profile\u001b[38;5;241m.\u001b[39mto_json()\n\u001b[1;32m      4\u001b[0m json_data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(json_data)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ProfileReport' is not defined"
     ]
    }
   ],
   "source": [
    "# enable once ydata-profiling supports 3.11\n",
    "profile = ProfileReport(df_credit, title=\"Data Profile Report\")\n",
    "json_data = profile.to_json()\n",
    "json_data = json.loads(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "297f077c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0p/jtlt6ynn0l5bwr8p7p_1sq200000gn/T/ipykernel_60817/3864164685.py:2: FutureWarning:\n",
      "\n",
      "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
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+Xd2SecF2s2bNMn7c//47bUK6/M+/A39+1CGZMumNTJvyVnrsvH123vWAzJ49O0my8krLp0mTJjn9tONy8sl9sv+Pf5al2rfLQw/enubNm9fXKwL1oHCYcPPNN2fZZZfNGWeckXvuuSf33HNPzjjjjHTt2jW///3v87Of/Sy//e1vc9FFF33hPWbOnDlfZ8PMmTO/1osAfFs1qZr3f8pvufGO3P7nuzJs6PCc1btv3npzRA78yX4NXB0sGp564rnstM0PslePgzJwwJPpf9Nl881h+P1vb0yPbffLj/c5InOrq3Nl/74NVC3UrwMO2CdTJr1R82nevFlZn3fb7Xdlo016ZPsd9s2bb47I7bf1r5nF0KRJk7Ro0SInnXR2Hn7k3/nPc4Nz0MHH5LurrpTtt7O8FhqTwv+X4pZbbslll12WH/3oRzXH9thjj6yzzjq59tprM2DAgCy//PK58MILc8YZZyzwHn379s15551X61ifPn1y5rE9i5YD0Gh8+OHkzJkzJx071v4NacdOHTL+f36781/jx02c7zeqnTp1mO83O1/mv785euP1t2odf/P1EfnOcsvU+T7Q2E36cErmzJmTDh1rdxV07Lj0fB0+/zVh/MRaw9+SpEPHpTNhfO1uhRmfzMiokaMzauToDB40NE8OeiAHHLxvrrr8szkMkydNyeRJUzLi7Xfy1hsjMuiVR7Phxuvlhedfqqc3hIZx330P57nnXqz5uWXLFkmSzp07ZuzY8TXHO3fqkCEvvbLAe0ycOClz5syZr7OuU6eOGfs//w6cNm16pk2bnrfeGpln/zM4E8e/mr333jl33PH3fPDBuCTJq8PfrHXviRMnZbnlzMxgIeiYL5vCnQlPP/101l9//rW866+/fp55Zt4axq222iqjR4/+wnv07t07U6dOrfXp3bt30VIAGpXZs2fnpSGvZJvtNq85VlVVlW223TzPPzdkgdcMem5Ittl281rHtt1+iwz63H/UfZXR77yXD94fl1W+W3vLrpVXXTHvjn6/7i8Ajdzs2bMzdMir2WrbzWqOVVVVZattNv3Cv9C/8NyQWucnyTbbb54Xnh/ypc+qalKVFi1afMn38/4T6svOgUXFRx99nLffHlXzefXVN/LBB+Oyw/Zb1ZyzxBKLZ5NN1s+z/3lhgfeYPXt2Bg8eWuuaqqqq7LD9Vnn22QVf899zqqqq0rLFvM6Ep58ZlCRZfbXPtots375dOnRYKqNHv/e13hOoX4U7E5ZbbrnccMMN8y1juOGGG7LccvPW7X744Ydp3779gi5PkrRs2XKB28rMnl60Gr5Jn3wyI6Pf++wvJmPeH5fX3ng7bZdcIst8wfo5+Lb5/VU35ar+v8mQF1/O4EFD8/NjDkmbNq1z+5/uTJJcfe3F+eD9cfnVeZclSa79/S2598E/5ZhjD8/D/xyYfffbLd3WXzu9jj+75p7t2rfNd76zbLosM+/P2ar/FxqMHzch4//vt7FX/fb6nNb7+Lwy7LW8PGx49j9wn3x3tZVzeM/jvsnXh7K77ppbcvk1v87QF1/Ji4OH5cijD07rxVrnjj/fnSS58ve/zgcfjM9F51+RJLnh2j/l//3j5hz1i0Pyr4cfz1777pJ1u62dU088N0nSuk3rnHDyz/Lwg49l3LgJWWqp9jn0iAPSZZnO+cff/5kkWX/DdbLeBuvk+WcGZ8rUqVlxxeXzyzOPy8gRo78ylIBF1W9/d33O6H183nxrREaNejfnnfvLvP/+uPz9//5cJMnDD92Re/7+YK75/c1JksuvvC433XB5Xhg8NM8//2KOP+7ILLZY69x8yx1J5g12/NEP98wjj/w7EyZ+mO90XTannvqLzJjxaR58aECS5M03R+Tv9z6Ufv3Oy9FHn5Zp06fnwl/1zmuvv5XH/mdgMdCwCocJl156aX74wx/mwQcfzMYbb5wkGTRoUIYPH54775z3H8vPP/989t9///qtlAb38mtv5vDjTqv5+eLfzdvmbq9duufCs05uqLKgUbnnrgeydIelcvoZx6dT5455edjw/OgHP60ZAPed7yxTa0Dt88+9mKN+enLOOPvEnNmnV0a8PSo9D/xFXvtce+fOu+yQq/r/pubn62++Iklycd/f5eK+v0uSXHvNLWnZsmV+1feMtGvfNq+8/Fr22+uwjBr57jfw1vDNuffuh7JUh6VyyhnHpmOnDnll2Gv5yX5H1QxlXPY7y6S6+rNdrwc9NyTHHnlqTj3z+Jx29okZOeKd/PQnx+X14fOWBVXPnZtVvrtS/vDjvbLU0u0zedKUvPTiy9l3155547W3kyQzZnyaXXfvnlNO/0Vat2md8eMmZOCAJ3Plpddm1qzZ3/z/EOAbcMml12Sxxdqk/zUXp127JfPUU89ntz1+UmvO2corr5AOn5st8re/3ZuOHZbKueecki5dOuall17Jbrv/pCb4/vTTmdlqy01y/HFHpH37thk3bmKeePLZbL3tXrUGpR562Am57NJzc+/fb0l1dSmPP/FMdtv9J3ZPgUamqlQqlb76tNpGjRqV/v3754035u2rvfrqq+eoo47KRx99lLXXXnuhi5k9ccRCXwt8seYdVk6SdFhytQauBCrTxGnz/n3Ytf1aDVwJVKYxk+et02/Wwpp5KIc5s8Y0dAllM+PvFzd0CQul9V6nNnQJX2mhRrWuuOKKNcscpk2blttvvz37779/Bg0alLlz59ZrgQAAAEDjUngA4389/vjjOeSQQ7Lsssvmsssuy/bbb59nn322PmsDAAAAGqFCnQljx47NzTffnBtuuCHTpk3Lj370o8ycOTP33HNP1lxzzXLVCAAAAMXZGrJs6tyZsMcee2T11VfP0KFDc8UVV+T999/P7373u3LWBgAAADRCde5MePDBB3P88cfn6KOPzne/+91y1gQAAAA0YnXuTHjyySczffr0bLjhhtl0001z1VVXZeLEieWsDQAAAGiE6hwmbLbZZrnuuuvywQcf5Kijjspf/vKXLLvssqmurs4jjzyS6dOnl7NOAAAAKKZUvWh+FgGFd3NYbLHFcvjhh+fJJ5/MsGHDcvLJJ+eiiy5Kp06dsueee5ajRgAAAKARWeitIZNk9dVXz8UXX5z33nsvt99+e33VBAAAADRiXytM+K+mTZtm7733zr333lsftwMAAAAasTrv5gAAAACLlOpFY/7AoqheOhMAAACAbw9hAgAAAFCIZQ4AAABUJsscykZnAgAAAFCIMAEAAAAoRJgAAAAAFGJmAgAAAJWpVGroCiqWzgQAAACgEGECAAAAUIhlDgAAAFQmW0OWjc4EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKpOZCWWjMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAIDKVDIzoVx0JgAAAACFCBMAAACAQixzAAAAoDLZGrJsdCYAAADAIu7qq6/OiiuumFatWmXTTTfNc88996XnX3HFFVl99dXTunXrLLfccjnppJPy6aef1vl5wgQAAABYhN1xxx3p1atX+vTpk8GDB2e99dZLjx49Mn78+AWef9ttt+X0009Pnz59Mnz48Nxwww254447csYZZ9T5mcIEAAAAWIT169cvRx55ZA477LCsueaa6d+/f9q0aZMbb7xxgec//fTT2XLLLXPggQdmxRVXzE477ZQDDjjgK7sZPk+YAAAAQGUqlRbJz8yZMzNt2rRan5kzZy7wFWfNmpUXXngh3bt3rznWpEmTdO/ePc8888wCr9liiy3ywgsv1IQHI0aMyAMPPJBdd921zv+jFSYAAABAI9K3b9+0bdu21qdv374LPHfixImZO3duOnfuXOt4586dM3bs2AVec+CBB+b888/PVlttlebNm2eVVVbJdtttZ5kDAAAALKp69+6dqVOn1vr07t273u4/cODA/PrXv84111yTwYMH56677sr999+fCy64oM73sDUkAAAAlWkR3RqyZcuWadmyZZ3O7dChQ5o2bZpx48bVOj5u3Lh06dJlgdecffbZOfjgg3PEEUckSdZZZ518/PHH+dnPfpYzzzwzTZp8dd+BzgQAAABYRLVo0SIbbrhhBgwYUHOsuro6AwYMyOabb77Aaz755JP5AoOmTZsmSUqlUp2eqzMBAAAAFmG9evXKIYccko022iibbLJJrrjiinz88cc57LDDkiQ9e/ZM165da+Yu7LHHHunXr1/WX3/9bLrppnnrrbdy9tlnZ4899qgJFb6KMAEAAAAWYfvvv38mTJiQc845J2PHjk23bt3y0EMP1QxlHD16dK1OhLPOOitVVVU566yzMmbMmHTs2DF77LFHLrzwwjo/s6pU1x6Gb8DsiSMaugSoSM07rJwk6bDkag1cCVSmidPeSJJ0bb9WA1cClWnM5FeSJM1adG3gSqAyzZk1pqFLKJsZN5zS0CUslNY/vbShS/hKZiYAAAAAhQgTAAAAgEKECQAAAEAhBjACAABQmUrVDV1BxdKZAAAAABQiTAAAAAAKscwBAACAilSqLjV0CRVLZwIAAABQiDABAAAAKESYAAAAABRiZgIAAACVqdrWkOWiMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAIDKVDIzoVx0JgAAAACFCBMAAACAQixzAAAAoDJVlxq6goqlMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAIDKVG1ryHLRmQAAAAAUIkwAAAAACrHMAQAAgMpkmUPZ6EwAAAAAChEmAAAAAIUIEwAAAIBCzEwAAACgMpVKDV1BxdKZAAAAABQiTAAAAAAKESYAAAAAhZiZAAAAQGWqrm7oCiqWzgQAAACgEGECAAAAUIhlDgAAAFSmaltDlovOBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACpTydaQ5aIzAQAAAChEmAAAAAAUYpkDAAAAlcnWkGWjMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAICKVKq2NWS56EwAAAAAChEmAAAAAIUIEwAAAIBCzEwAAACgMlWXGrqCiqUzAQAAAChEmAAAAAAUYpkDAAAAlalka8hy0ZkAAAAAFCJMAAAAAAoRJgAAAACFmJkAAABAZbI1ZNnoTAAAAAAKaVSdCc07rNzQJUBFmzjtjYYuASramMmvNHQJUNHmzBrT0CUA8H8aVZgAAAAA9aba1pDl0qjChA5LrtbQJUBF+m9HwuyJIxq4EqhM/+2sO3PFAxu4EqhMF466LUly8Ar7NnAlUJn++M5dDV0CiyAzEwAAAIBChAkAAABAIY1qmQMAAADUG1tDlo3OBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACpTqbqhK6hYOhMAAACAQoQJAAAAQCGWOQAAAFCZbA1ZNjoTAAAAgEKECQAAAEAhwgQAAACgEDMTAAAAqEilaltDlovOBAAAAKAQYQIAAABQiGUOAAAAVCZbQ5aNzgQAAACgEGECAAAAUIgwAQAAACjEzAQAAAAqk5kJZaMzAQAAAChEmAAAAAAUIkwAAAAACjEzAQAAgMpUqm7oCiqWzgQAAACgEGECAAAAUIhlDgAAAFQmW0OWjc4EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKlLJzISy0ZkAAAAAFCJMAAAAAAoRJgAAAACFmJkAAABAZTIzoWx0JgAAAACFCBMAAACAQixzAAAAoDJVVzd0BRVLZwIAAABQiDABAAAAKESYAAAAABRiZgIAAACVydaQZaMzAQAAAChEmAAAAAAUYpkDAAAAlckyh7LRmQAAAAAUIkwAAAAAChEmAAAAAIWYmQAAAEBFKpXMTCgXnQkAAABAIcIEAAAAoBBhAgAAAFCImQkAAABUpmozE8pFZwIAAABQiDABAAAAKMQyBwAAACqTZQ5lozMBAAAAKESYAAAAABQiTAAAAAAKMTMBAACAilQyM6FsdCYAAAAAhQgTAAAAgEIscwAAAKAyWeZQNjoTAAAAgEKECQAAAEAhwgQAAACgEDMTAAAAqEzVDV1A5dKZAAAAABQiTAAAAAAKESYAAAAAhZiZAAAAQEUqVZcauoSKpTMBAAAAKESYAAAAABRimQMAAACVyTKHstGZAAAAABQiTAAAAAAKESYAAAAAhZiZAAAAQGWqbugCKpfOBAAAAKAQYQIAAABQiGUOAAAAVKSSrSHLRmcCAAAAUIgwAQAAAChEmAAAAAAUYmYCAAAAlcnWkGWjMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAICKVKouNXQJFUtnAgAAAFCIMAEAAAAoxDIHAAAAKpOtIctGZwIAAABQiDABAAAAKESYAAAAABRiZgIAAAAVqWRmQtnoTAAAAAAKKdyZMHfu3Nx9990ZPnx4kmSNNdbI3nvvnWbNNDkAAADAt0GhBOCVV17JnnvumbFjx2b11VdPkvzmN79Jx44dc99992XttdcuS5EAAABQmGUOZVNomcMRRxyRtdZaK++9914GDx6cwYMH59133826666bn/3sZ+WqEQAAAGhECnUmDBkyJIMGDUr79u1rjrVv3z4XXnhhNt5443ovjvI4/MiDcuzxP02nzh3zysuv5fRfXpAXXxj6hefvuffO6X3WiVlu+a4Z8faonN/n0vzr4X/XfL/bHjvl0J/+OOt1WytLLdU+2225V14eNny++2y0SbecefZJ2WCj9VI9tzovDxueH+5zeD79dGZZ3hMWNYOGDMtNt/2/vPraW5nw4aRc2ffsfH+bLRq6LFgkbHrwjtn6qN2zeMe2GTt8dP7R55a899LbCzx3ox9vn/X33TqdV18uSTJm2Mg8cskdtc5frMOS2fn0A7Lq1uum1ZJtMuq51/KPPrfkw1Fjv5H3gcame8+ds+vP9k7bju3y7vBRubXP9Rnx0lsLPHe7H3fPVj/YLt9Zffkkychhb+dvF/+51vkb7bxpdjioR1ZcZ5Us0X6JnLlLr4x+ddQ38SpAPSnUmbDaaqtl3Lhx8x0fP358Vl111XorivLZe99dc8Gve+eSi67KDlvvnVeGvZa/3XVDOnRYaoHnb7zJ+vnDjf3y51v/lu232jsP3P+v3Hrb1fneGt+tOafNYq3zn2deyPnnXPqFz91ok27565035LFHn8pO2++XHbf7Qa7/w59SXa3vCP5rxoxPs/qqK+fMk49p6FJgkbLO7ptl17N+kkevvCtX73Zmxr46OofeenoWW3rJBZ6/0mZrZui9T+eGA36V/vv2ydQPPsyhfzw9S3b+7JclP/nDyWm/XKf86cjLcvVuZ2TKmIk57E+907x1y2/qtaDR2HT3LXPgWYfl7iv/mrN3PyWjh4/KqX88J0su3XaB56+x+dp55t4n8+sfn5Pz9umdSe9/mFP/2CftO3/235stW7fKG88Pzx0X/fGbeg2gnn1lZ8K0adNq/rlv3745/vjjc+6552azzTZLkjz77LM5//zz85vf/KZ8VVJvjj72sPzxlr/m9j/flSQ5+cRzsmOP7XLgwfvlt5f/Yb7zjzr6kDz6rydy1W9vSJJc9Ksrs932W+aIn/0kp5zUJ0nyt7/8PUmy3PJdv/C5v+p7Rv5w7a21nvHWWyPr7b2gEmy9+cbZenNdXlDUlkfsmkF/eSyD/zava+7vZ96Q1Xfolg1/tG0e//19853/txOvrvXz3af9IWvtvHFW3nLtDLnriSy9Upcsv8F3c+WOv8z4N8ckSe4988ac/vw1WW/PzTPojoFlfydoTHY5Yo8M/MsjeeJvjyZJbjrj2qy3w4bZ5kc75B+/v3u+839/whW1fr7+tGuy8S6bZc0t181Tdw1Mkjx197w/rx2+07GstYOtIcvnKzsT2rVrl/bt26d9+/bZY4898uqrr+ZHP/pRVlhhhaywwgr50Y9+lJdffjl77LHHN1EvX0Pz5s2zXre18u/Hnq45ViqV8u+BT2fjTbot8JqNNumWfw98utaxxwY8mY02Wb/Oz+3QYalstHG3TJwwKQ888pe8+tbTufeBP2XTzTZcqPcAgP9q2rxpll17pbz11Ms1x0qlUt566uUsv8F3v+TKzzRv3TJNmzfLjCkfJUmatWieJJkzc3ate86ZNScrbLx6PVYPjV/T5s2y4jqr5JUnP1sSWyqV8sqTQ7PqBnX789CydYs0bd40H0+ZXq4ygQbwlZ0Jjz322DdRB9+ApZdun2bNmmXChIm1jk8YPzHfXW3lBV7TqXOHTBhf+/zx4yemU+cOdX7uCivNW5N6au9j0+fM3+TlYcOz/wF75677bsnWm+2WEW+/U/BNAGCeNu2XSNNmTfPRxKm1jn80YWo6rrJsne6x8+kHZNq4yXn7/wKJCW+/n8nvTchOp/4495xxQ2bP+DRb/HTXtFt26SzRqf1X3A0qyxL/92ds6sQptY5Pmzgly67yxV2pn7d/756ZPG5yXnnqi2d0AYuerwwTtt1223p/6MyZMzNzZu2hey1bWoNYqZpUzWuAueXGO2qWVwwbOjxbb7t5DvzJfvnVeZc1ZHkAfIttc/QeWWePzXP9jy+o6USonjM3t/38iux78ZE5e+h1mTtnbt5+6uW8/tiQVFU1cMGwiNn96H2y2R5b5tf7n5PZn+v2Aerf1VdfnUsuuSRjx47Neuutl9/97nfZZJNNvvD8KVOm5Mwzz8xdd92VSZMmZYUVVsgVV1yRXXfdtU7PK7Sbw38feMMNN2T48HnT+tdaa60cfvjhadt2wQNYFqRv374577zzah3r06dP0VIo6MMPJ2fOnDnp2LF2V0HHTh0yftyEBV4zftzEdOxU+/xOnTpk/LiJCzx/Qcb9373feL32xN83Xx+R7yy3TJ3vAwD/65PJ0zN3ztws3qH2f4cs3rFtPpow5Uuv3erI3bLN0XvmpoN+nXGvvVvru/dfHpmrdj0jLZdonabNm+WTSdPz83vOz5ihI+r7FaBRm/5/f8badmhX6/iSHdplylf8Gdv1Z3tl96P3zW8OOjfvvqYTlQbyLZmZcMcdd6RXr17p379/Nt1001xxxRXp0aNHXn/99XTq1Gm+82fNmpUdd9wxnTp1yv/7f/8vXbt2zTvvvJN27drV+ZmFdnMYNGhQVllllVx++eWZNGlSJk2alH79+mWVVVbJ4MGD63yf3r17Z+rUqbU+vXv3LlIKC2H27Nl5acgr2Wa7zWuOVVVVZZttN8/zzw1Z4DWDnhuSbbbdvNaxbbffIoOee7HOzx39znv54P1xWeW7K9U6vvKqK+bd0e/X/QUA4H/MnT037788MqtssVbNsaqqqqyyxVoZPfjNL7xu66N2z/bH7ZNbDvlNxgz74oHAM6fPyCeTpmfpFbuk6zorZ/gjL9Rr/dDYzZ09J6OGvZ01t1y35lhVVVXW2nLdvDX49S+8brej9s5ex+2XSw65ICOHLXibVqD+9OvXL0ceeWQOO+ywrLnmmunfv3/atGmTG2+8cYHn33jjjZk0aVLuueeebLnllllxxRWz7bbbZr311qvzMwt1Jpx00knZc889c91116VZs3mXzpkzJ0cccUROPPHEPP7443W6T8uWLS1raCC/v+qmXNX/Nxny4ssZPGhofn7MIWnTpnVu/9OdSZKrr704H7w/rmbpwbW/vyX3PvinHHPs4Xn4nwOz7367pdv6a6fX8WfX3LNd+7b5zneWTZdl5iVeq/5faDB+3ISM/795C1f99vqc1vv4vDLstXkzEw7cJ99dbeUc3vO4b/L1oVH75JMZGf3eZwHbmPfH5bU33k7bJZfIMl3mT5SBeZ66/oH84LKfZ8ywEXlvyNvZ4qe7pEWbVnnh/3Z32O+yozNt3KQ8fPEdSZKtf75Hup+0X/56wlWZ/N6ELN5xXlfDrI8/zaxP5i3DXHvXTfPxpGmZMubDdPnectmtT8+8+vCgvPXEsIZ5SWhAD15/X3522XEZOfStjHjpzfQ4fI+0bNMyj//f7g5H9Ts+k8d+mL9e/OckyW4/3yc/6PXjXHPC5Zn43vi07dguSfLpx59m5iefJkkWa7t4lu7aoWa7yGVWnjd/YeqEKZn6FR0P8G3wRaMBFvT36FmzZuWFF16o9Qv6Jk2apHv37nnmmWcWeP977703m2++eX7xi1/k73//ezp27JgDDzwwp512Wpo2bVqnGguFCYMGDaoVJCRJs2bNcuqpp2ajjTYqcisayD13PZClOyyV0884Pp06d8zLw4bnRz/4aSZM+DBJ8p3vLJPq6s96gZ5/7sUc9dOTc8bZJ+bMPr0y4u1R6XngL/La8M9+27PzLjvkqv6fbQ16/c1XJEku7vu7XNz3d0mSa6+5JS1btsyv+p6Rdu3b5pWXX8t+ex2WUSNrt5XCt9nLr72Zw487rebni383byvVvXbpngvPOrmhyoJGb9g/ns1iSy2Z75+0X5bo2C4fDH8nNx9yUT6eOG9767Zdl07pc3uDbfqT7mnWsnkO7H9SrfsMuOLOPHrFvHB9iU7tsstZP8niHdpm+vjJGXLXk3nsd3d9cy8Fjch//vFUllh6yfyg1wFp27FdRr86Mpf0vCDT/m/w6dLLdkjpc//9+P2f9Ejzls1zQv9Ta93nrsvvyN1XzAv1Nthx4/zsss9+qXTs1SfPdw7Uh0V1a8gvGg1w7rnnznfuxIkTM3fu3HTu3LnW8c6dO+e1115b4P1HjBiRRx99NAcddFAeeOCBvPXWWznmmGMye/bsOo8gqCqVSqW6vc68Yv74xz9mp512qnX8n//8Z3r27Jlx48bV9VYL1GHJ1b7W9cCCTZz2RpJk9kRrfaEcmneYtyPOmSse2MCVQGW6cNRtSZKDV9i3gSuByvTHdyo3LJ2wY/1vKPBNWPIfD9e5M+H9999P165d8/TTT2fzzT9bon7qqafm3//+d/7zn//Md81qq62WTz/9NCNHjqzpROjXr18uueSSfPDBB3WqsVBnwv7775+f/vSnufTSS7PFFlskSZ566qn88pe/zAEHHFDkVgAAAMACFBkN0KFDhzRt2nS+X+6PGzcuXbp0WeA1yyyzTJo3b15rScMaa6yRsWPHZtasWWnRosVXPrdQmHDppZemqqoqPXv2zJw5c1IqldKiRYscffTRueiii4rcCgAAAPiaWrRokQ033DADBgzI3nvvnSSprq7OgAEDcuyxxy7wmi233DK33XZbqqur06TJvH0Z3njjjSyzzDJ1ChKSgrs5tGjRIldeeWUmT56cIUOG5KWXXsqkSZNy+eWXG6gIAABAo1KqXjQ/RfXq1SvXXXddbrnllgwfPjxHH310Pv744xx22GFJkp49e9Ya0Hj00Udn0qRJOeGEE/LGG2/k/vvvz69//ev84he/qPMz69SZsO++X70+rVmzZunSpUt23HHH7LHHHnUuAAAAAFh4+++/fyZMmJBzzjknY8eOTbdu3fLQQw/VDGUcPXp0TQdCkiy33HL55z//mZNOOinrrrtuunbtmhNOOCGnnXbaFz1iPnUKE9q2bfuV51RXV+fNN9/M9ddfn1NOOSXnn39+nYsAAAAAFt6xxx77hcsaBg4cON+xzTffPM8+++xCP69OYcJNN91U5xv+4x//yDHHHCNMAAAAgApVaABjXWy11VbZaKON6vu2AAAAUMjCzB+gbgoNYKyLdu3a5a67KnefUgAAAPi2q/cwAQAAAKhs9b7MAQAAABqFUlVDV1CxdCYAAAAAhQgTAAAAgEKECQAAAEAhZiYAAABQkWwNWT46EwAAAIBChAkAAABAIZY5AAAAUJFK1baGLBedCQAAAEAhwgQAAACgEGECAAAAUIiZCQAAAFQkW0OWj84EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKlKpVNXQJVQsnQkAAABAIcIEAAAAoBDLHAAAAKhItoYsH50JAAAAQCHCBAAAAKAQYQIAAABQiJkJAAAAVKRSta0hy0VnAgAAAFCIMAEAAAAoxDIHAAAAKlKp1NAVVC6dCQAAAEAhwgQAAACgEGECAAAAUIiZCQAAAFQkW0OWj84EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKpKZCeWjMwEAAAAoRJgAAAAAFGKZAwAAABWpVGroCiqXzgQAAACgEGECAAAAUIgwAQAAACjEzAQAAAAqkq0hy0dnAgAAAFCIMAEAAAAoxDIHAAAAKlKpZJlDuehMAAAAAAoRJgAAAACFCBMAAACAQsxMAAAAoCKVqhu6gsqlMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAICKVF2qaugSKpbOBAAAAKAQYQIAAABQiGUOAAAAVKSSZQ5lozMBAAAAKESYAAAAABQiTAAAAAAKMTMBAACAilSqNjOhXHQmAAAAAIUIEwAAAIBChAkAAABAIWYmAAAAUJFKpYauoHLpTAAAAAAKESYAAAAAhVjmAAAAQEWyNWT56EwAAAAAChEmAAAAAIUIEwAAAIBCzEwAAACgIlWXzEwoF50JAAAAQCHCBAAAAKAQyxwAAACoSCXLHMpGZwIAAABQiDABAAAAKESYAAAAABRiZgIAAAAVqVRq6Aoql84EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKlJ1qaqhS6hYOhMAAACAQoQJAAAAQCGWOQAAAFCRSpY5lI3OBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACpSqdTQFVQunQkAAABAIcIEAAAAoBDLHAAAAKhI1baGLBudCQAAAEAhwgQAAACgkKpSyXxLAAAAKs+g7+zd0CUslI3eu6ehS/hKZiYAAABQkUpmJpRNowoTurZfq6FLgIo0ZvIrSZIzVzywgSuBynThqNuSJLMnjmjgSqAyNe+wcpJk8g+2a9hCoEK1v3NgQ5fAIsjMBAAAAKAQYQIAAABQSKNa5gAAAAD1pdrMhLLRmQAAAAAUIkwAAAAACrHMAQAAgIpUaugCKpjOBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACqSrSHLR2cCAAAAUIgwAQAAACjEMgcAAAAqUskyh7LRmQAAAAAUIkwAAAAAChEmAAAAAIWYmQAAAEBFqm7oAiqYzgQAAACgEGECAAAAUIgwAQAAACjEzAQAAAAqUilVDV1CxdKZAAAAABQiTAAAAAAKscwBAACAilRdaugKKpfOBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACpSta0hy0ZnAgAAAFCIMAEAAAAoxDIHAAAAKlLJMoey0ZkAAAAAFCJMAAAAAAoRJgAAAACFmJkAAABARapu6AIqmM4EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKlIpVQ1dQsXSmQAAAAAUIkwAAAAACrHMAQAAgIpka8jy0ZkAAAAAFCJMAAAAAAoRJgAAAACFmJkAAABARTIzoXx0JgAAAACFCBMAAACAQoQJAAAAQCFmJgAAAFCRSqlq6BIqls4EAAAAoBBhAgAAAFCIZQ4AAABUpGqrHMpGZwIAAABQiDABAAAAKESYAAAAABRiZgIAAAAVqdrWkGWjMwEAAAAopHCYcPjhh2f69OnzHf/4449z+OGH10tRAAAAQONVOEy45ZZbMmPGjPmOz5gxI7feemu9FAUAAABfV2kR/SwK6jwzYdq0aSmVSimVSpk+fXpatWpV893cuXPzwAMPpFOnTmUpEgAAAGg86hwmtGvXLlVVVamqqspqq6023/dVVVU577zz6rU4AAAAoPGpc5jw2GOPpVQqZYcddsidd96ZpZZaqua7Fi1aZIUVVsiyyy5bliIBAACAxqPOYcK2226bJBk5cmSWW265NGliIwgAAAAar+qGLqCC1TlM+K8VVlghU6ZMyXPPPZfx48enurr2//f07Nmz3ooDAAAAGp/CYcJ9992Xgw46KB999FGWXHLJVFVV1XxXVVUlTAAAAIAKV3itwsknn5zDDz88H330UaZMmZLJkyfXfCZNmlSOGgEAAIBGpHCYMGbMmBx//PFp06ZNOeoBAACAelFdVbVIfhbG1VdfnRVXXDGtWrXKpptumueee65O1/3lL39JVVVV9t5770LPKxwm9OjRI4MGDSp6GQAAAFAGd9xxR3r16pU+ffpk8ODBWW+99dKjR4+MHz/+S68bNWpUTjnllGy99daFn1l4ZsJuu+2WX/7yl3n11VezzjrrpHnz5rW+33PPPQsXAQAAACycfv365cgjj8xhhx2WJOnfv3/uv//+3HjjjTn99NMXeM3cuXNz0EEH5bzzzssTTzyRKVOmFHpm4TDhyCOPTJKcf/75831XVVWVuXPnFr0lAAAA1LtSQxewkGbOnJmZM2fWOtayZcu0bNlyvnNnzZqVF154Ib1796451qRJk3Tv3j3PPPPMFz7j/PPPT6dOnfLTn/40TzzxROEaCy9zqK6u/sKPIAEAAAC+nr59+6Zt27a1Pn379l3guRMnTszcuXPTuXPnWsc7d+6csWPHLvCaJ598MjfccEOuu+66ha6xcGcCAAAAUD69e/dOr169ah1bUFfCwpg+fXoOPvjgXHfddenQocNC36dwmLCg5Q2fd8455yx0MQAAAPBt90VLGhakQ4cOadq0acaNG1fr+Lhx49KlS5f5zn/77bczatSo7LHHHjXHqqurkyTNmjXL66+/nlVWWeUrn1s4TLj77rtr/Tx79uyMHDkyzZo1yyqrrCJMAAAAoFGobugCvgEtWrTIhhtumAEDBtRs71hdXZ0BAwbk2GOPne/8733vexk2bFitY2eddVamT5+eK6+8Msstt1ydnls4THjxxRfnOzZt2rQceuih2WeffYreDgAAAPgaevXqlUMOOSQbbbRRNtlkk1xxxRX5+OOPa3Z36NmzZ7p27Zq+ffumVatWWXvttWtd365duySZ7/iXqZeZCUsuuWTOO++87LHHHjn44IPr45YAAABAHey///6ZMGFCzjnnnIwdOzbdunXLQw89VDOUcfTo0WnSpPD+C1+q3gYwTp06NVOnTq2v2wEAAMDXUl3V0BV8c4499tgFLmtIkoEDB37ptTfffHPh5xUOE37729/W+rlUKuWDDz7IH//4x+yyyy6FCwAAAAAWLYXDhMsvv7zWz02aNEnHjh1zyCGHpHfv3vVWGAAAANA4FQ4TRo4cWY46AAAAgEXE15qZ8N577yVJvvOd79RLMQAAAFBfqvMtGprwDSs8zrG6ujrnn39+2rZtmxVWWCErrLBC2rVrlwsuuCDV1d+GXTwBAADg261wZ8KZZ56ZG264IRdddFG23HLLJMmTTz6Zc889N59++mkuvPDCei8SAAAAaDwKhwm33HJLrr/++uy55541x9Zdd9107do1xxxzjDABAAAAKlzhMGHSpEn53ve+N9/x733ve5k0aVK9FAUAAABfV6mhC6hghWcmrLfeernqqqvmO37VVVdlvfXWq5eiAAAAgMarcGfCxRdfnN122y3/+te/svnmmydJnnnmmbz77rt54IEH6r1AAAAAoHEp3Jmw7bbb5o033sg+++yTKVOmZMqUKdl3333z+uuvZ+utty5HjQAAAFBYddWi+VkUFO5MSJJll13WoEUAAAD4llqoMOHTTz/N0KFDM378+FRXV9f67vO7PAAAAACVp3CY8NBDD6Vnz56ZOHHifN9VVVVl7ty59VIYAAAA0DgVnplw3HHH5Yc//GE++OCDVFdX1/oIEgAAAGgsqhfRz6KgcJgwbty49OrVK507dy5HPQAAAEAjVzhM2G+//TJw4MAylAIAAAAsCgrPTLjqqqvywx/+ME888UTWWWedNG/evNb3xx9/fL0VBwAAAAur1NAFVLDCYcLtt9+ehx9+OK1atcrAgQNTVfXZJphVVVXCBAAAAKhwhcOEM888M+edd15OP/30NGlSeJUEAAAAsIgrnAbMmjUr+++/vyABAAAAvqUKJwKHHHJI7rjjjnLUAgAAAPWmumrR/CwKCi9zmDt3bi6++OL885//zLrrrjvfAMZ+/frVW3EAAABA41M4TBg2bFjWX3/9JMnLL79c67vPD2Ok8TrkiANy9HGHpWOnDnn15ddz9mm/zpDBw77w/N332im/POO4fGf5rhk54p38+tx+efSRJ2q+73XaMdlr312ybNcumTV7doYNeTW/+dWVefGFz+55021XZa11vpelOyyVqVOm5cl/P5MLz+2XcWMnlPVdobHY9OAds/VRu2fxjm0zdvjo/KPPLXnvpbcXeO5GP94+6++7dTqvvlySZMywkXnkkjtqnb9YhyWz8+kHZNWt102rJdtk1HOv5R99bsmHo8Z+I+8Di6JBQ4blptv+X1597a1M+HBSrux7dr6/zRYNXRYsElruvHda7vXjNGm3VOaOeiuf3PDbzH3rtS88v6rN4ml14E/TYrNtUrX4EqmeMC6f3HRV5gz+T5Kk1T4Hpvlm26Rp1+VTmjUzc15/JTP+eG2q33/3m3ol4GsqHCY89thj5aiDb8ie++ycPr86Naf3Oi8vvjAsR/z84Pz5zmuzzca758OJk+Y7f6NNuuXq6y9J3/OvyL/++e/ss99uueFPv8vO2+2X14e/lSQZ8fY7OevUC/POqPfSqnXLHHl0z9x213XZcoNdMunDyUmSp594Lr/r94eMGzchyyzTOWdfcEr+cMvl2avHT77R94eGsM7um2XXs36Sv591Y9598a1sefguOfTW03P5Difn4w+nzXf+SputmaH3Pp3Rg9/M7Jmzs83P98ihfzw9v93x1EwbN+/P1E/+cHLmzp6TPx15WWZ+NCNbHrFrDvtT71y546mZPWPmN/2KsEiYMePTrL7qytlnt51y4hm/auhyYJHRfIvt0/rQY/LJtf0y583habX7fln87Esy7biDU5o2Zf4LmjXL4n0uTWnq5Hx0SZ+UJk1Mk46dU/r4o89OWatbZj50T+a89VqqmjRN64OOyOLnXJJpJxyazPz0G3s3YOFVlUqlRrP1Ztf2azV0CRXvvkduz0svvpyzTr0wybxukudfHpCbrrstV19x/Xzn//6GS9NmsdY55Me/+OweD9+WV15+Laf3On+Bz1h8icXy+ujnsv9eh+fJx/+zwHN23GX73Pin32alzutnzpw59fBmfJkxk19Jkpy54oENXMm308/vOT9jXhqR+/rcnGTen7tTn/ldnrnln3n89/d95fVVTapy1kvX5b4+t2TIXU9k6ZW6pNdj/XLljr/M+DfH1Nzz9OevySOX3JFBdwws49uwIBeOui1JMnviiAauhLpae8tddCYsQpp3WDlJMvkH2zVsId9SS/S9JnPefj0zrr9y3oGqqrS99q/59MG7M/Pu2+Y7v8VOe6bVXvtn2vE9k7lz6/SMqiXbpt1Nf8/0s4/PnFeH1mf51EH7Owc2dAllc913Fs1fXh753p8auoSvVLgzIUkGDRqUv/71rxk9enRmzZpV67u77rqrXgqj/jVv3jzrdlszV11+Xc2xUqmUJ//9bDbceL0FXrPhJt3yh6tvqXVs4KNPZefdvv+FzzjokB9m6tRpeeXl1xd4Trt2bbPvfrtl0HNDBAlUvKbNm2bZtVfKv6+5t+ZYqVTKW0+9nOU3+G6d7tG8dcs0bd4sM6bM+41OsxbzZtXMmTm71j3nzJqTFTZeXZgAQP1p1ixNV1k9n34+NCiVMnvoC2m22ppZUC9ci423yJzXX02bI09M8423TGna1Mx64l/59J7bk+rqBT6mqs3iSZLq6dPL8BJAORTezeEvf/lLtthiiwwfPjx33313Zs+enVdeeSWPPvpo2rZtW44aqSdLLd0uzZo1y8QJH9Y6PmHCh+nYqcMCr+nYqUMm/M/5Eyd8mI6dlq51rHuPbfPGu89nxNjBOfLonjlgnyMzedKUWueccW6vvPne83ll5NPp+p1lcviBx379l4JGrk37JdK0WdN8NHFqreMfTZiaxTu2q9M9dj79gEwbNzlvPzVvTs2Et9/P5PcmZKdTf5xWSy6Wps2bZuuf75F2yy6dJTq1r+9XAOBbrGqJtqlq2jTVU2ovhy1NnZwm7ZZa4DVNOi+bFptvmzRpko8uPD0z/nZrWu75o7T6wcFf8JCqtD7s2MwZPizV746s71cAyqRwmPDrX/86l19+ee677760aNEiV155ZV577bX86Ec/yvLLL1+ne8ycOTPTpk2r9Zk50xrfRdlTTzyXnbb5QfbqcVAGDngy/W+6LEt3qP0vmN//9sb02Ha//HifIzK3ujpX9u/bQNXComObo/fIOntsnj8f1a+mE6F6ztzc9vMr0mHlLjl76HXpM/zmrLz5mnn9sSEpfcFvfADgG1NVldLUyfmk/2WZO+KNzH76sXx655/SsseeCzy9zZEnpunyK+WjfgteQgtfR/Ui+lkUFF7m8Pbbb2e33XZLkrRo0SIff/xxqqqqctJJJ2WHHXbIeeed95X36Nu373zn9enTp2gpFDTpwymZM2dOOnSs3VXQsePSmTB+4gKvmTB+Yjr+z/kdOi6dCeNrdyvM+GRGRo0cnVEjR2fwoKF5ctADOeDgfXPV5Z/NYZg8aUomT5qSEW+/k7feGJFBrzyaDTdeLy88/1I9vSE0Pp9Mnp65c+Zm8Q61O7cW79g2H02Y8qXXbnXkbtnm6D1z00G/zrjXak+3fv/lkblq1zPSconWadq8WT6ZNH3ebIah1uwDUH9K06emNHfuvF0cPne8qm37+boV/qt68ofzZiV8LuCe+947adJ+6aRZs+Rzy1xbH3FCmm+4eaaffXxKk+zyBYuSwp0J7du3z/T/W8vUtWvXmu0hp0yZkk8++aRO9+jdu3emTp1a69O7d++ipVDQ7NmzM3TIq9lq281qjlVVVWWrbTb9wr/Qv/DckFrnJ8k222+eF54f8qXPqmpSlRYtWnzJ9/P+V+/LzoFKMHf23Lz/8sisssVnA2arqqqyyhZrZfTgN7/wuq2P2j3bH7dPbjnkNxkz7ItbPmdOn5FPJk3P0it2Sdd1Vs7wR16o1/oB+JabMydz3349zdbZ4LNjVVVpvu6GmfPGqwu+5LWX06RL1+Rz28Y3XXa5VE+aOF+Q0GKTrTL93JNSPd7WxrCoKdyZsM022+SRRx7JOuuskx/+8Ic54YQT8uijj+aRRx7J97+/4KF8/6tly5Zp2bJl4WL5+q675pZcfs2vM/TFV/Li4GE58uiD03qx1rnjz3cnSa78/a/zwQfjc9H5VyRJbrj2T/l//7g5R/3ikPzr4cez1767ZN1ua+fUE89NkrRu0zonnPyzPPzgYxk3bkKWWqp9Dj3igHRZpnP+8fd/JknW33CdrLfBOnn+mcGZMnVqVlxx+fzyzOMycsTorwwloBI8df0D+cFlP8+YYSPy3pC3s8VPd0mLNq3ywt/+nSTZ77KjM23cpDx88R1Jkq1/vke6n7Rf/nrCVZn83oQs3nFeV8Osjz/NrE/mLQlbe9dN8/GkaZky5sN0+d5y2a1Pz7z68KC89cSwhnlJWAR88smMjH7v/Zqfx7w/Lq+98XbaLrlElunSqQErg8bt0/v+lsWO6525b79eszVkWrbKrEcfTJK0Oa53qidNzKd/njfke+Y//55Wu+yT1ocfl5kP3JUmy3wnrfY9KDMf+GxQe+sjT0yLrbvn44vOTGnGjFT93/yF0icfJf8z4B1onAqHCVdddVU+/XTe3q9nnnlmmjdvnqeffjo/+MEPctZZZ9V7gdSve+9+KEt1WCqnnHFsOnbqkFeGvZaf7HdUzVDGZb+zTKqrP9stdNBzQ3Lskafm1DOPz2lnn5iRI97JT39yXF4f/laSpHru3Kzy3ZXyhx/vlaWWbp/Jk6bkpRdfzr679swbr72dZN6+3rvu3j2nnP6LtG7TOuPHTcjAAU/mykuvzaxZs+cvEirMsH88m8WWWjLfP2m/LNGxXT4Y/k5uPuSifDxxWpKkbdelUyp91gq66U+6p1nL5jmw/0m17jPgijvz6BV3JkmW6NQuu5z1kyzeoW2mj5+cIXc9mcd+Zzcd+DIvv/ZmDj/utJqfL/7dH5Ike+3SPReedXJDlQWN3uynH8uMtu3S6seHzVvuMPKtfPSrU1OaOjlJ0qRD5+Rzu82XPpyQ6Rf8Mm0OOzYt+92Y6kkTMvP+O+ft5vB/Wu28d5JkiQuurPWsj6+6KLMee6j8L8W3Rqnqq89h4VSVSp/7k9/AurZf66tPAgobM/mVJMmZKx7YwJVAZbpw1Lwt02ZPNLMCyqF5h5WTJJN/sF3DFgIVqv2dAxu6hLLpv9xPGrqEhfLzd//U0CV8pcIzEwAAAIBvN2ECAAAAUEjhmQkAAACwKKj+6lNYSDoTAAAAgEIKhwmHH354pk+fPt/xjz/+OIcffni9FAUAAAA0XoXDhFtuuSUzZsyY7/iMGTNy66231ktRAAAA8HVVL6KfRUGdZyZMmzYtpVIppVIp06dPT6tWrWq+mzt3bh544IF06tSpLEUCAAAAjUedw4R27dqlqqoqVVVVWW211eb7vqqqKuedd169FgcAAAA0PnUOEx577LGUSqXssMMOufPOO7PUUkvVfNeiRYussMIKWXbZZctSJAAAANB41DlM2HbbbZMkI0eOzPLLL5+qqqqyFQUAAABfV6mhC6hgdQoThg4dmrXXXjtNmjTJ1KlTM2zYsC88d91116234gAAAIDGp05hQrdu3TJ27Nh06tQp3bp1S1VVVUql+TOeqqqqzJ07t96LBAAAABqPOoUJI0eOTMeOHWv+GQAAABq7aqvzy6ZOYcIKK6ywwH8GAAAAvn3qFCbce++9db7hnnvuudDFAAAAAI1fncKEvffeu9bP/zsz4fM7O5iZAAAAAJWtSV1Oqq6urvk8/PDD6datWx588MFMmTIlU6ZMyQMPPJANNtggDz30ULnrBQAAgDqpXkQ/i4I6dSZ83oknnpj+/ftnq622qjnWo0ePtGnTJj/72c8yfPjwei0QAAAAaFzq1JnweW+//XbatWs33/G2bdtm1KhR9VASAAAA0JgVDhM23njj9OrVK+PGjas5Nm7cuPzyl7/MJptsUq/FAQAAAI1P4WUON954Y/bZZ58sv/zyWW655ZIk7777br773e/mnnvuqe/6AAAAYKEsKvMHFkWFw4RVV101Q4cOzSOPPJLXXnstSbLGGmuke/futXZ1AAAAACpT4TAhmbcV5E477ZRtttkmLVu2FCIAAADAt0jhmQnV1dW54IIL0rVr1yy++OIZOXJkkuTss8/ODTfcUO8FAgAAwMIoLaKfRUHhMOFXv/pVbr755lx88cVp0aJFzfG11147119/fb0WBwAAADQ+hcOEW2+9NX/4wx9y0EEHpWnTpjXH11tvvZoZCgAAAEDlKhwmjBkzJquuuup8x6urqzN79ux6KQoAAABovAoPYFxzzTXzxBNPZIUVVqh1/P/9v/+X9ddfv94KAwAAgK+j2l4BZVM4TDjnnHNyyCGHZMyYMamurs5dd92V119/Pbfeemv+8Y9/lKNGAAAAoBEpvMxhr732yn333Zd//etfWWyxxXLOOedk+PDhue+++7LjjjuWo0YAAACgESnUmTBnzpz8+te/zuGHH55HHnmkXDUBAADA11bd0AVUsEKdCc2aNcvFF1+cOXPmlKseAAAAoJErvMzh+9//fv7973+XoxYAAABgEVB4AOMuu+yS008/PcOGDcuGG26YxRZbrNb3e+65Z70VBwAAADQ+hcOEY445JknSr1+/+b6rqqrK3Llzv35VAAAA8DWVGrqAClY4TKiuNsICAAAAvs0Kz0wAAAAAvt3qHCY8+uijWXPNNTNt2rT5vps6dWrWWmutPP744/VaHAAAAND41DlMuOKKK3LkkUdmySWXnO+7tm3b5qijjsrll19er8UBAADAwqpOaZH8LArqHCa89NJL2Xnnnb/w+5122ikvvPBCvRQFAAAANF51DhPGjRuX5s2bf+H3zZo1y4QJE+qlKAAAAKDxqnOY0LVr17z88stf+P3QoUOzzDLL1EtRAAAA8HVVL6KfRUGdw4Rdd901Z599dj799NP5vpsxY0b69OmT3XffvV6LAwAAABqfZnU98ayzzspdd92V1VZbLccee2xWX331JMlrr72Wq6++OnPnzs2ZZ55ZtkIBAACAxqHOYULnzp3z9NNP5+ijj07v3r1TKs2bMFlVVZUePXrk6quvTufOnctWKAAAANA41DlMSJIVVlghDzzwQCZPnpy33norpVIp3/3ud9O+ffty1QcAAAALZdHYZHHRVChM+K/27dtn4403ru9aAAAAgEVAnQcwAgAAACQL2ZkAAAAAjd2iss3iokhnAgAAAFCIMAEAAAAoRJgAAAAAFGJmAgAAABWpuqqhK6hcOhMAAACAQoQJAAAAQCHCBAAAAKAQMxMAAACoSNUpNXQJFUtnAgAAAFCIMAEAAAAoxDIHAAAAKpJFDuWjMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAICKVN3QBVQwnQkAAABAIcIEAAAAoBDLHAAAAKhI1TaHLBudCQAAAEAhwgQAAACgEGECAAAAUIiZCQAAAFQkExPKR2cCAAAAUIgwAQAAAChEmAAAAAAUYmYCAAAAFam6oQuoYDoTAAAAgEKECQAAAEAhljkAAABQkaptDlk2OhMAAACAQoQJAAAAQCHCBAAAAKAQMxMAAACoSCYmlI/OBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACpSdUMXUMF0JgAAAACFCBMAAACAQixzAAAAoCKVbA5ZNjoTAAAAgEKECQAAAEAhwgQAAACgEDMTAAAAqEi2hiwfnQkAAABAIcIEAAAAoBDLHAAAAKhI1baGLBudCQAAAEAhwgQAAACgEGECAAAAUIiZCQAAAFQkExPKR2cCAAAAUIgwAQAAACikqlQq6fwAAACg4hy94o8auoSF8vtRf23oEr6SmQkAAABUpGpTE8qmUYUJzVp0begSoCLNmTUmSXLwCvs2cCVQmf74zl1Jksk/2K5hC4EK1f7OgUmS2RNHNGwhUKGad1i5oUtgEWRmAgAAAFBIo+pMAAAAgPpS3dAFVDCdCQAAAEAhwgQAAACgEGECAAAAUIiZCQAAAFSkkq0hy0ZnAgAAAFCIMAEAAAAoxDIHAAAAKpKtIctHZwIAAABQiDABAAAAKESYAAAAABRiZgIAAAAVydaQ5aMzAQAAAChEmAAAAAAUIkwAAAAACjEzAQAAgIpU3dAFVDCdCQAAAEAhwgQAAACgEMscAAAAqEjVJVtDlovOBAAAAKAQYQIAAABQiDABAAAAKMTMBAAAACqSiQnlozMBAAAAKESYAAAAABRimQMAAAAVqdpCh7LRmQAAAAAUIkwAAACARdzVV1+dFVdcMa1atcqmm26a55577gvPve6667L11lunffv2ad++fbp37/6l5y+IMAEAAAAWYXfccUd69eqVPn36ZPDgwVlvvfXSo0ePjB8/foHnDxw4MAcccEAee+yxPPPMM1luueWy0047ZcyYMXV+pjABAACAilRaRP9fUf369cuRRx6Zww47LGuuuWb69++fNm3a5MYbb1zg+X/+859zzDHHpFu3bvne976X66+/PtXV1RkwYECdnylMAAAAgEZk5syZmTZtWq3PzJkzF3jurFmz8sILL6R79+41x5o0aZLu3bvnmWeeqdPzPvnkk8yePTtLLbVUnWsUJgAAAEAj0rdv37Rt27bWp2/fvgs8d+LEiZk7d246d+5c63jnzp0zduzYOj3vtNNOy7LLLlsrkPgqtoYEAACARqR3797p1atXrWMtW7Ysy7Muuuii/OUvf8nAgQPTqlWrOl8nTAAAAKAiVTd0AQupZcuWdQ4POnTokKZNm2bcuHG1jo8bNy5dunT50msvvfTSXHTRRfnXv/6Vddddt1CNljkAAADAIqpFixbZcMMNaw1P/O8wxc033/wLr7v44otzwQUX5KGHHspGG21U+Lk6EwAAAGAR1qtXrxxyyCHZaKONsskmm+SKK67Ixx9/nMMOOyxJ0rNnz3Tt2rVm7sJvfvObnHPOObntttuy4oor1sxWWHzxxbP44ovX6ZnCBAAAACpS9UJss7go2n///TNhwoScc845GTt2bLp165aHHnqoZijj6NGj06TJZwsTfv/732fWrFnZb7/9at2nT58+Offcc+v0TGECAAAALOKOPfbYHHvssQv8buDAgbV+HjVq1Nd+npkJAAAAQCHCBAAAAKAQyxwAAACoSKVvycyEhqAzAQAAAChEmAAAAAAUYpkDAAAAFam6oQuoYDoTAAAAgEKECQAAAEAhwgQAAACgEDMTAAAAqEilkq0hy0VnAgAAAFCIMAEAAAAoRJgAAAAAFGJmAgAAABWpOmYmlIvOBAAAAKAQYQIAAABQiGUOAAAAVKTqhi6ggulMAAAAAAoRJgAAAACFCBMAAACAQsxMAAAAoCKVbA1ZNjoTAAAAgEKECQAAAEAhwgQAAACgEDMTAAAAqEjVZiaUjc4EAAAAoBBhAgAAAFCIZQ4AAABUpFLJMody0ZkAAAAAFCJMAAAAAAoRJgAAAACFmJkAAABARapu6AIqmM4EAAAAoBBhAgAAAFCIZQ4AAABUpFJsDVkuOhMAAACAQoQJAAAAQCHCBAAAAKAQMxMAAACoSNVmJpSNzgQAAACgEGECAAAAUMhCL3OYMmVKnnvuuYwfPz7V1dW1vuvZs+fXLgwAAABonBYqTLjvvvty0EEH5aOPPsqSSy6Zqqqqmu+qqqqECQAAADS4UsnMhHJZqGUOJ598cg4//PB89NFHmTJlSiZPnlzzmTRpUn3XCAAAADQiCxUmjBkzJscff3zatGlT3/UAAAAAjdxCLXPo0aNHBg0alJVXXrm+6wEAAIB6YWvI8lmoMGG33XbLL3/5y7z66qtZZ5110rx581rf77nnnvVSHAAAAND4LFSYcOSRRyZJzj///Pm+q6qqyty5c79eVQAAAECjtVBhwv9uBQkAAAB8eyxUmAAAAACNXcnMhLJZqN0ckuTf//539thjj6y66qpZddVVs+eee+aJJ56oz9oAAACARmihwoQ//elP6d69e9q0aZPjjz8+xx9/fFq3bp3vf//7ue222+q7RgAAAKARWahlDhdeeGEuvvjinHTSSTXHjj/++PTr1y8XXHBBDjzwwHorEAAAABZGdckyh3JZqM6EESNGZI899pjv+J577pmRI0d+7aIAAACAxmuhwoTlllsuAwYMmO/4v/71ryy33HJfuygAAACg8VqoZQ4nn3xyjj/++AwZMiRbbLFFkuSpp57KzTffnCuvvLJeCwQAAAAal4UKE44++uh06dIll112Wf76178mSdZYY43ccccd2Wuvveq1QAAAAFgYJiaUz0KFCUmyzz77ZJ999qnPWgAAAIBFwELNTAAAAAC+vercmbDUUkvljTfeSIcOHdK+fftUVVV94bmTJk2ql+IAAACAxqfOYcLll1+eJZZYouafvyxMAAAAgIZWbWpC2dQ5TDjkkENq/vnQQw8tRy0AAADAImChZiY0bdo048ePn+/4hx9+mKZNm37tovhmnNvnlLz7zuBMn/pW/vngX7Lqqit95TVH//yQvPXGs/lo2tt5+sn7svFG3Wp9f83Vv8nrw5/K9Klv5YMxQ3PXnTdm9dVXme8+PQ/+UQa/8Eg+mvZ23n/vpfz2ygvr67WgUerec+f0e7J/bnj9Lzn3nouy8nqrfuG52/24e87626/Sf+it6T/01pz25z7znb/Rzpvm1D+ek2uG3JI/vnNXll9zxTK/ATRuLXfeO0v+/i9pd/vDWaLvNWm66ve+9PyqNoun9REnpO31d6bdXx7Okr/7Y5ptsGnN9632OTBL/KZ/2v3pgbS98e4sdtqv0mTZ5cr9GrBIGzRkWH5xap9sv+dBWXvLXTLg8acbuiSgjBYqTCiVFtwqMnPmzLRo0eJrFcQ345enHJNjf3F4jjn29Gyx1R75+JNP8sA//pyWLVt+4TU//OGeufSSPrngV/2y8aY756Whr+aB+/+cjh2Xrjln8OChOeLIXll73e2y624HpqqqKg/ef3uaNPnsf9VOPOFnueD8U3PxJVdn3W47pMcuP87Djwws5+tCg9p09y1z4FmH5e4r/5qzdz8lo4ePyql/PCdLLt12geevsfnaeebeJ/PrH5+T8/bpnUnvf5hT/9gn7TsvVXNOy9at8sbzw3PHRX/8pl4DGq3mW2yf1ocek0//enOm/fLIzH3n7Sx+9iWpWrLdgi9o1iyL97k0TTt1yUeX9Mm043rmk99fmtKHEz87Za1umfnQPZnW+5h8dN4pqWraNIufc0nSstU381KwCJox49OsvurKOfPkYxq6FKhRndIi+VkUVJW+KBlYgN/+9rdJkpNOOikXXHBBFl988Zrv5s6dm8cffzyjRo3Kiy++uFDFNGvRdaGuo7h33xmcy6+4Nv0uvzZJsuSSS+T994bk8CNOyl//eu8Cr3n6yfvy/KCXcsKJZyVJqqqqMmrE87n6mpty8SVXL/CaddZZIy++8K+s9r0tMmLEO2nXrm1Gj3ohe+9zaB597MnyvBzzmTNrTJLk4BX2beBKvp3OveeijBj6Vm495/ok8/7sXPHsH/LIzQ/kH7+/+yuvr2rSJNcOvTW3nHN9nrprYK3vOnynYy5/6tqcuUuvjH51VBmqpy7++M5dSZLJP9iuYQv5llqi7zWZ8/brmXH9lfMOVFWl7bV/zacP3p2Zd9823/ktdtozrfbaP9OO75nMnVunZ1Qt2Tbtbvp7pp99fOa8OrQ+y6cO2t85MEkye+KIhi2EOlt7y11yZd+z8/1ttmjoUqiD5h1WbugSymbzrts3dAkL5ZkxjzV0CV+pzjMTknmDF5N5nQn9+/evtaShRYsWWXHFFdO/f//6rZB6t9JKy2eZZTpnwKOf/WV+2rTpee65F7PZphsuMExo3rx5Nthg3Vx08VU1x0qlUgY8+mQ222zDBT6nTZvWObTn/hkx4p28++77SZLu3bdJkyZVWbZrlwwbOjBLLL54nnl2UH556vl577336/lNoeE1bd4sK66zSu675q6aY6VSKa88OTSrbrB6ne7RsnWLNG3eNB9PmV6uMmHR1axZmq6yej79fGhQKmX20BfSbLU1M3MBl7TYeIvMef3VtDnyxDTfeMuUpk3NrCf+lU/vuT2prl7gY6razPsFSvV0fw4BICkYJowcOTJJsv322+euu+5K+/bty1IU5dWlc6ckybhxE2odHzd+Yrp06bTAazp0WCrNmjXL+HETax0fP35Cvvc/MxF+ftQhuajvmVl88cXy2utvZeddD8js2bOTJCuvtHyaNGmS0087Lr16nZOpU6fn/PNOzUMP3p71N+hecx5UiiXaL5GmzZpm6sQptY5Pmzgly65St26s/Xv3zORxk/PKU34bCv+raom2qWraNNVTam9LXZo6OU27Lr/Aa5p0XjbN1u6SWU88ko8uPD1NunRNm5+dmDRtlk//dssCHlKV1ocdmznDh6X63ZFleAsAWPQs1MyExx577GsFCTNnzsy0adNqfWbOXNDvDqgPBxywT6ZMeqPm07x5oQypsNtuvysbbdIj2++wb958c0Ruv61/zSyGJk2apEWLFjnppLPz8CP/zn+eG5yDDj4m3111pWy/nTY4+F+7H71PNttjy1z5s99k9kxhG9SLqqqUpk7OJ/0vy9wRb2T204/l0zv/lJY99lzg6W2OPDFNl18pH/U7/xsuFICvq1QqLZKfRcFC/63yvffey7333pvRo0dn1qxZtb7r16/fl17bt2/fnHfeebWO9enTZ2FL4Svcd9/Dee65z+ZYtGw5b0hm584dM3bsZ7tydO7UIUNeemWB95g4cVLmzJmTTp071DreqVPHjP2fDodp06Zn2rTpeeutkXn2P4Mzcfyr2XvvnXPHHX/PBx+MS5K8OvzNWveeOHFSllvOzAwqz/TJ0zN3zty07dCu1vElO7TLlAlTvvTaXX+2V3Y/et/85qBz8+5r75SvSFiElaZPTWnu3DRpt1Q+P/2gqm37+boV/qt68ofzZiV8bknD3PfeSZP2SyfNmiVz5tQcb33ECWm+4eaZfvbxKU2asKDbAcC30kJ1JgwYMCCrr756fv/73+eyyy7LY489lptuuik33nhjhgwZ8pXX9+7dO1OnTq316d2798KUQh189NHHefvtUTWfV199Ix98MC47bL9VzTlLLLF4Ntlk/Tz7nxcWeI/Zs2dn8OChta6pqqrKDttvlWefXfA1/z2nqqoqLVvM60x4+plBSZLVV/tsaUT79u3SocNSGT36va/1ntAYzZ09J6OGvZ01t1y35lhVVVXW2nLdvDX49S+8brej9s5ex+2XSw65ICOHvf1NlAqLpjlzMvft19NsnQ0+O1ZVlebrbpg5b7y64EteezlNunRNqqpqjjVddrlUT5o4X5DQYpOtMv3ck1I9fmzZXgEAFkULFSb07t07p5xySoYNG5ZWrVrlzjvvzLvvvpttt902P/zhD7/y+pYtW2bJJZes9fmyLQmpf7/93fU5o/fx2X33HbP22t/LzTddmfffH5e///2fNec8/NAdOeboQ2t+vvzK63LETw/MwQf/MN/73qq5+qqLsthirXPzLXckmTfY8bRTj80G66+T5ZZbNptvtlHu+Mu1mTHj0zz40IAkyZtvjsjf730o/fqdl8032yhrrbV6brrxirz2+lt5bKC9iKlMD15/X7b7cfds9YPtsuyqXXPohUelZZuWefxvjyZJjup3fH506kE15+/2833yg5MPyHWnXp2J741P247t0rZju7Rs89mWdIu1XTzLr7liun533r73y6zcNcuvuWLadmz3jb4bNAaf3ve3tOy+e1ps1yNNui6fNj87KWnZKrMefTBJ0ua43ml10JE158/859/TZPEl0vrw49Jkme+k2QabpdW+B2XmQ/fUnNP6yBPTYpsd8/EVv0ppxoxUtVsqVe2WSmyBDV/ok09m5LU33s5rb8wLwce8Py6vvfF2PvhcJyx80xp6i8dK3hpyoZY5DB8+PLfffvu8GzRrlhkzZmTxxRfP+eefn7322itHH310vRZJ/bvk0muy2GJt0v+ai9Ou3ZJ56qnns9seP6k1u2LllVdIhw6f7Wv/t7/dm44dlsq555ySLl065qWXXsluu/8k48fPG8r46aczs9WWm+T4445I+/ZtM27cxDzx5LPZetu9MmHChzX3OfSwE3LZpefm3r/fkurqUh5/4pnstvtPMudzvw2CSvKffzyVJZZeMj/odUDadmyX0a+OzCU9L8i0iVOTJEsv2yGlz7Vbf/8nPdK8ZfOc0P/UWve56/I7cvcV88K7DXbcOD+77Lia7469+uT5zoFvi9lPP5YZbdul1Y8Pm7fcYeRb+ehXp6Y0dXKSpEmHzsnn1p+WPpyQ6Rf8Mm0OOzYt+92Y6kkTMvP+O+ft5vB/Wu28d5JkiQuurPWsj6+6KLMee6j8LwWLoJdfezOHH3dazc8X/+4PSZK9dumeC886uaHKAsqkqrQQ0x26dOmSxx57LGussUbWXHPNXHTRRdlzzz3z0ksvZcstt8xHH320UMU0a2HNPJTDnFljkiQHr7BvA1cClemP78zb+nPyD7Zr2EKgQrW/c2CSZPbEEQ1bCFSo5h1WbugSymaTZbdt6BIWynPv/7uhS/hKC9WZsNlmm+XJJ5/MGmuskV133TUnn3xyhg0blrvuuiubbbZZfdcIAAAANCILFSb069evpvvgvPPOy0cffZQ77rgj3/3ud79yJwcAAAD4JpQWkfkDi6LCYcLcuXPz3nvvZd11500mX2yxxdK/f/96LwwAAABonArv5tC0adPstNNOmTx5cjnqAQAAABq5hdoacu21186IEQbgAAAAwLfRQs1M+NWvfpVTTjklF1xwQTbccMMstthitb5fcskl66U4AAAAWFgLsXkhdbRQYcKuu+6aJNlzzz1TVVVVc7xUKqWqqipz586tn+oAAACARmehwoTHHnusvusAAAAAFhELFSZsu+229V0HAAAA1KtqW0OWzUKFCY8//viXfr/NNtssVDEAAABA47dQYcJ2220337HPz04wMwEAAAAq10JtDTl58uRan/Hjx+ehhx7KxhtvnIcffri+awQAAAAakYXqTGjbtu18x3bccce0aNEivXr1ygsvvPC1CwMAAICvw9aQ5bNQnQlfpHPnznn99dfr85YAAABAI7NQnQlDhw6t9XOpVMoHH3yQiy66KN26dauPugAAAIBGaqHChG7duqWqqmq+lpHNNtssN954Y70UBgAAADROCxUmjBw5stbPTZo0SceOHdOqVat6KQoAAAC+ruqYmVAuhcOE6urqDBgwIHfddVdGjRqVqqqqrLTSStlvv/1y8MEH19oiEgAAAKg8hQYwlkql7LnnnjniiCMyZsyYrLPOOllrrbXyzjvv5NBDD80+++xTrjoBAACARqJQZ8LNN9+cxx9/PAMGDMj2229f67tHH300e++9d2699db07NmzXosEAACAokqWOZRNoc6E22+/PWecccZ8QUKS7LDDDjn99NPz5z//ud6KAwAAABqfQmHC0KFDs/POO3/h97vsskteeumlr10UAAAA0HgVChMmTZqUzp07f+H3nTt3zuTJk792UQAAAEDjVWhmwty5c9Os2Rdf0rRp08yZM+drFwUAAABfV3XJzIRyKRQmlEqlHHrooWnZsuUCv585c2a9FAUAAAA0XoXChEMOOeQrz7GTAwAAAFS2QmHCTTfdVK46AAAAoF7ZGrJ8Cg1gBAAAABAmAAAAAIUIEwAAAIBCCs1MAAAAgEWFrSHLR2cCAAAAUIgwAQAAAChEmAAAAAAUYmYCAAAAFakUMxPKRWcCAAAAUIgwAQAAACjEMgcAAAAqkq0hy0dnAgAAAFCIMAEAAAAoRJgAAAAAFGJmAgAAABXJ1pDlozMBAAAAKESYAAAAABRimQMAAAAVydaQ5aMzAQAAAChEmAAAAAAUIkwAAAAACjEzAQAAgIpka8jy0ZkAAAAAFCJMAAAAAAoRJgAAAACFmJkAAABARSqVqhu6hIqlMwEAAAAoRJgAAAAAFGKZAwAAABWp2taQZaMzAQAAAChEmAAAAAAUIkwAAAAACjEzAQAAgIpUKpmZUC46EwAAAIBChAkAAABAIZY5AAAAUJFsDVk+OhMAAACAQoQJAAAAQCHCBAAAAKAQMxMAAACoSLaGLB+dCQAAAEAhwgQAAACgEGECAAAAUIiZCQAAAFSkajMTykZnAgAAAFCIMAEAAAAoxDIHAAAAKlIpljmUi84EAAAAoBBhAgAAAFCIMAEAAAAoxMwEAAAAKlLJ1pBlozMBAAAAKESYAAAAABRimQMAAAAVqdrWkGWjMwEAAAAoRJgAAAAAFCJMAAAAAAoxMwEAAICKZGvI8tGZAAAAABQiTAAAAAAKESYAAAAAhZiZAAAAQEWqNjOhbHQmAAAAAIUIEwAAAIBCLHMAAACgItkasnx0JgAAAACFCBMAAACAQoQJAAAAQCHCBAAAACpSdUqL5GdhXH311VlxxRXTqlWrbLrppnnuuee+9Py//e1v+d73vpdWrVplnXXWyQMPPFDoecIEAAAAWITdcccd6dWrV/r06ZPBgwdnvfXWS48ePTJ+/PgFnv/000/ngAMOyE9/+tO8+OKL2XvvvbP33nvn5ZdfrvMzhQkAAACwCOvXr1+OPPLIHHbYYVlzzTXTv3//tGnTJjfeeOMCz7/yyiuz884755e//GXWWGONXHDBBdlggw1y1VVX1fmZwgQAAABoRGbOnJlp06bV+sycOXOB586aNSsvvPBCunfvXnOsSZMm6d69e5555pkFXvPMM8/UOj9JevTo8YXnL4gwAQAAgIpUKpUWyU/fvn3Ttm3bWp++ffsu8B0nTpyYuXPnpnPnzrWOd+7cOWPHjl3gNWPHji10/oI0q/OZAAAAQNn17t07vXr1qnWsZcuWDVTNggkTAAAAoBFp2bJlncODDh06pGnTphk3blyt4+PGjUuXLl0WeE2XLl0Knb8gVaVSaeH2nQAAAIBGbPE2KzV0CQvlo09GFjp/0003zSabbJLf/e53SZLq6uosv/zyOfbYY3P66afPd/7++++fTz75JPfdd1/NsS222CLrrrtu+vfvX6dnmplAYTNnzsy55577hQNAgK/HnzEoL3/GoLz8GYNvXq9evXLdddfllltuyfDhw3P00Ufn448/zmGHHZYk6dmzZ3r37l1z/gknnJCHHnool112WV577bWce+65GTRoUI499tj/3969B1VR/n8Afx9uBwQveANFlBAklItBkKImmAYDEiiaY4mipHhDSDS8MCqkjiPe09ApFHUaLyWKWWCGqIgmOChCHoUcKSPoIpgdNeXy/P7wx35dweQgcETfL+bMsLvP7n6eM/vMnvPZ53lOg8/Jngmksdu3b6N9+/b4+++/0a5dO22HQ/TCYRsjal5sY0TNi22MnicvS88EANi8eTPi4+NRVlaG/v37Y9OmTXjjjTcAAJ6enrCyskJSUpJU/ssvv0RMTAyKi4tha2uL1atXw9fXt8HnYzKBNMYbBFHzYhsjal5sY0TNi22MnicvUzKhpXECRiIiIiIiInohCfDZeXPhnAlEREREREREpBEmE0hjSqUSS5cufe5+55ToRcE2RtS82MaImhfbGNHLgXMmEBERERER0QvJyKiXtkNolHv3ftZ2CE/FnglEREREREREpBEmE4iIiIiIiIhII0wmEBEREREREZFGmEwgInqOnThxAgqFArdu3dJ2KERapVAocOjQIQBAcXExFAoFLl68qNWYiOjZJCUloUOHDtoOg15wQohW+WoNmEwgmbNnz0JXVxd+fn7aDoXohRUSEoLAwEBth0H0TMrKyhAeHg5ra2solUpYWlrC398f6enpzX5uS0tLlJaWwsHBAcDLk3R7WepJTS8kJAQKhQIKhQL6+vowMzPDiBEjsH37dtTU1LRIDFZWVtiwYYNs3bhx41BYWNgi5yeipsdkAskkJiYiPDwcp06dwm+//abtcIiI6DlUXFwMV1dXHD9+HPHx8cjPz0daWhq8vLwwa9asJ+5XWVnZJOfX1dWFubk59PT0muR4RC8DHx8flJaWori4GKmpqfDy8kJERARGjhyJqqqqRh1TCNHofQHAyMgIXbt2bfT+RKRdTCaQRK1WY9++fZgxYwb8/PyQlJQk23748GHY2trC0NAQXl5e2LlzZ50nJKdPn8aQIUNgZGQES0tLzJkzB3fu3GnZihC1Ivfv38ecOXPQtWtXGBoaYvDgwcjJyalTLisrC05OTjA0NMSAAQNQUFCghWiJHpo5cyYUCgWys7MRFBSEPn36oF+/fpg7dy5++OEHqZxCoUBCQgLeeecdGBsbY8WKFQCAlJQUuLi4wNDQENbW1oiNjZV9ISkqKsKbb74JQ0ND9O3bF8eOHZOd/9FhDsXFxfDy8gIAmJqaQqFQICQkpN64b968ifHjx8PCwgJt2rSBo6Mj9uzZIyvj6emJ8PBwREZGwtTUFGZmZvjss89w584dTJ48GW3btoWNjQ1SU1Nl+508eRLu7u5QKpXo1q0bFixYIKtTfU9l+/fvj2XLlsner88//xyjRo1CmzZtYGtri8OHD0t1bmg9ieqjVCphbm4OCwsLuLi4YNGiRUhJSUFqaiqSkpLqHT5069YtKBQKnDhxAsD/esekpqbC1dUVSqUSp0+fxrVr1xAQEAAzMzOYmJjAzc0N33//vXQcT09P/Pzzz/jwww+lHhJA/cMcEhIS0Lt3bxgYGMDOzg67d++Wbf+vdkJELYvJBJLs378fr776Kuzs7DBhwgRs375dGq9z/fp1jBkzBoGBgcjLy0NYWBgWL14s2//atWvw8fFBUFAQLl26hH379uH06dOYPXu2NqpD1Cp89NFHOHDgAHbu3Inc3FzY2NjA29sb5eXlsnLz58/H2rVrkZOTgy5dusDf37/JnvISaaK8vBxpaWmYNWsWjI2N62x//IvBsmXLMGrUKOTn52PKlCnIzMzExIkTERERgcuXL2Pbtm1ISkqSEg01NTUYPXo0DAwMcO7cOWzduhXR0dFPjMfS0hIHDhwAAFy9ehWlpaXYuHFjvWX//fdfuLq64ptvvkFBQQGmTZuG4OBgZGdny8rt3LkTnTt3RnZ2NsLDwzFjxgyMHTsWHh4eyM3Nxdtvv43g4GDcvXsXAFBSUgJfX1+4ubkhLy8PCQkJSExMxPLlyxv8vtaKjY3Fu+++i0uXLsHX1xfvv/8+ysvLNaonUUMNGzYMzs7OSE5O1mi/BQsWYNWqVVCpVHBycoJarYavry/S09Nx4cIF+Pj4wN/fH7/88gsAIDk5GT169EBcXBxKS0tRWlpa73EPHjyIiIgIREVFoaCgAGFhYZg8eTIyMjJk5Z7UTojqI1rpX6sgiP6fh4eH2LBhgxBCiMrKStG5c2eRkZEhhBAiOjpaODg4yMovXrxYABAVFRVCCCFCQ0PFtGnTZGUyMzOFjo6OuHfvXrPHT9RaTJo0SQQEBAi1Wi309fXFF198IW178OCB6N69u1i9erUQQoiMjAwBQOzdu1cqc/PmTWFkZCT27dvX4rETnTt3TgAQycnJTy0LQERGRsrWvfXWW2LlypWydbt37xbdunUTQghx9OhRoaenJ0pKSqTtqampAoA4ePCgEEKI69evCwDiwoULQoj/tZPa+5Em/Pz8RFRUlLQ8dOhQMXjwYGm5qqpKGBsbi+DgYGldaWmpACDOnj0rhBBi0aJFws7OTtTU1EhltmzZIkxMTER1dbUQQohevXqJ9evXy87t7Owsli5dKi0DEDExMdKyWq0WAERqauoz15NebrX3nfqMGzdO2Nvb12lXQghRUVEhAEifB2uvwUOHDj31nP369ROffPKJtFxfG9ixY4do3769tOzh4SGmTp0qKzN27Fjh6+srLT+tnRA9zkDZo1W+WgP2TCAAD59yZGdnY/z48QAAPT09jBs3DomJidJ2Nzc32T7u7u6y5by8PCQlJcHExER6eXt7o6amBtevX2+ZihC1IteuXUNlZSUGDRokrdPX14e7uztUKpWs7MCBA6X/O3bsCDs7uzpliFqC0HCG6ddff122nJeXh7i4ONm9YurUqSgtLcXdu3ehUqlgaWmJ7t27S/s8ev0/i+rqanz88cdwdHREx44dYWJigqNHj0pPT2s5OTlJ/+vq6qJTp05wdHSU1pmZmQEA/vjjDwCASqXCwIEDpa7bADBo0CCo1Wr8+uuvGsX46LmNjY3Rrl076TxEzUEIIbt2G+Lxdq1WqzFv3jzY29ujQ4cOMDExgUqlqtO2nkalUsnuicDDtvT4/Y7thOj5wJmLCMDDiRerqqpkH96EEFAqldi8eXODjqFWqxEWFoY5c+bU2dazZ88mi5WIiLTH1tYWCoUCV65caVD5x4dCqNVqxMbGYvTo0XXKGhoaNkmMTxIfH4+NGzdiw4YNcHR0hLGxMSIjI/HgwQNZOX19fdly7Qz4jy4D0GgWfB0dnTqJmPqGKtV37paabZ9eTiqVCq+88gp0dB4+Y3z0On3ScLrH2/W8efNw7NgxrFmzBjY2NjAyMsKYMWPqtK2mwnZCmtA0CU4Nx54JhKqqKuzatQtr167FxYsXpVdeXh66d++OPXv2wM7ODufPn5ft9/gkcS4uLrh8+TJsbGzqvAwMDFqySkStQu0EU1lZWdK6yspK5OTkoG/fvrKyj05qV1FRgcLCQtjb27dYrES1OnbsCG9vb2zZsqXeCXaf9rOFLi4uuHr1ar33Ch0dHdjb2+PGjRuyMdWPXv/1qb3HVFdX/2e5rKwsBAQEYMKECXB2doa1tXWT/Cydvb09zp49K/vAmpWVhbZt26JHjx4AgC5dusjqdPv2bY177TW0nkQNdfz4ceTn5yMoKAhdunQBANl1+uhkjP8lKysLISEhGDVqFBwdHWFubo7i4mJZGQMDg6deu/b29rJ7Yu2xH78nEtHzgckEwpEjR1BRUYHQ0FA4ODjIXkFBQUhMTERYWBiuXLmC6OhoFBYWYv/+/dKvPdQ+oYmOjsaZM2cwe/ZsXLx4EUVFRUhJSeEEjERPYGxsjBkzZmD+/PlIS0vD5cuXMXXqVNy9exehoaGysnFxcUhPT0dBQQFCQkLQuXNnBAYGaidweult2bIF1dXVcHd3x4EDB1BUVASVSoVNmzY9dUjCkiVLsGvXLsTGxuLHH3+ESqXC3r17ERMTAwAYPnw4+vTpg0mTJiEvLw+ZmZl1Jvx9XK9evaBQKHDkyBH8+eefUKvV9ZaztbXFsWPHcObMGahUKoSFheH3339v3JvwiJkzZ+LGjRsIDw/HlStXkJKSgqVLl2Lu3LnS095hw4Zh9+7dyMzMRH5+PiZNmgRdXV2NztPQehLV5/79+ygrK0NJSQlyc3OxcuVKBAQEYOTIkZg4cSKMjIwwYMAAaWLFkydPSu3yaWxtbZGcnCw9jHrvvffq9BSwsrLCqVOnUFJSgr/++qve48yfPx9JSUlISEhAUVER1q1bh+TkZMybN++Z609ETY/JBEJiYiKGDx+O9u3b19kWFBSE8+fP459//sFXX32F5ORkODk5ISEhQfpwp1QqATwcv3by5EkUFhZiyJAheO2117BkyRLZ0Akietg1Wk/v4SizVatWISgoCMHBwXBxccFPP/2Eo0ePwtTUVLbPqlWrEBERAVdXV5SVleHrr79mjx/SGmtra+Tm5sLLywtRUVFwcHDAiBEjkJ6ejoSEhP/c19vbG0eOHMF3330HNzc3DBgwAOvXr0evXr0APBwOcPDgQdy7dw/u7u744IMPpF96eBILCwvExsZiwYIFMDMze2ISOyYmBi4uLvD29oanpyfMzc2bJClnYWGBb7/9FtnZ2XB2dsb06dMRGhoq+yK2cOFCDB06FCNHjoSfnx8CAwPRu3dvjc/TkHoS1SctLQ3dunWDlZUVfHx8kJGRgU2bNiElJUVKbG3fvh1VVVVwdXVFZGRkg3+RZN26dTA1NYWHhwf8/f3h7e0NFxcXWZm4uDgUFxejd+/eUi+IxwUGBmLjxo1Ys2YN+vXrh23btmHHjh3w9PR8proTUfNQCA4ioUZasWIFtm7dihs3bmg7FKJWxcfHBzY2Ng2ej4SIiIiIGkffwELbITRK5YMSbYfwVJyAkRrs008/hZubGzp16oSsrCzEx8fzqQiRBioqKpCVlYUTJ05g+vTp2g6HiIiIiKjRmEygBisqKsLy5ctRXl6Onj17IioqCgsXLtR2WEStxpQpU5CTk4OoqCgEBARoOxwiIiIiokbjMAciIiIiIiJ6Iem10mEOVa1gmAMnYCQiIiIiIiIijTCZQEREREREREQaYTKBiIiIiIiIiDTCOROIiIiIiIiISCPsmUBEREREREREGmEygYiIiIiIiIg0wmQCEREREREREWmEyQQiIiIiIiIi0giTCURERERERESkESYTiIiIiIiIiEgjTCYQERERERERkUaYTCAiIiIiIiIijTCZQEREREREREQa+T9Lzxi6a46uUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 12))\n",
    "corr_plot = sns.heatmap(df_credit.corr(), linewidths=0.1, vmax=1.0, square=True, linecolor=\"white\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "584db9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "missing_values = df_credit.isnull().sum() * 100 / len(df_credit)\n",
    "missing_values = missing_values[missing_values > 0]\n",
    "missing_values.sort_values(inplace=True)\n",
    "missing_values = missing_values.to_frame()\n",
    "missing_values.columns = [\"% of missing\"]\n",
    "missing_values.index.names = [\"Attribute\"]\n",
    "missing_values[\"Attribute\"] = missing_values.index\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "missing_plot = sns.barplot(x=\"Attribute\", y=\"% of missing\", data=missing_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3dd7130a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "name": "Good credit",
         "type": "bar",
         "x": [
          "good"
         ],
         "y": [
          700
         ]
        },
        {
         "name": "Bad credit",
         "type": "bar",
         "x": [
          "bad"
         ],
         "y": [
          300
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
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    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Risk\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Risk\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Risk\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Risk\"].value_counts().values,\n",
    "    name=\"Bad credit\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout()\n",
    "\n",
    "layout = go.Layout(yaxis=dict(title=\"Count\"), xaxis=dict(title=\"Risk Variable\"), title=\"Target variable distribution\")\n",
    "\n",
    "plot1 = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(plot1, filename=\"grouped-bar\")"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb0d3b3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/songshuang/Desktop/dp-examples/.venv/lib/python3.12/site-packages/plotly/tools.py:455: DeprecationWarning:\n",
      "\n",
      "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n",
      "\n"
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       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_good = df_credit.loc[df_credit[\"Risk\"] == \"good\"][\"Age\"].values.tolist()\n",
    "df_bad = df_credit.loc[df_credit[\"Risk\"] == \"bad\"][\"Age\"].values.tolist()\n",
    "df_age = df_credit[\"Age\"].values.tolist()\n",
    "\n",
    "# First plot\n",
    "trace0 = go.Histogram(x=df_good, histnorm=\"probability\", name=\"Good Credit\")\n",
    "# Second plot\n",
    "trace1 = go.Histogram(x=df_bad, histnorm=\"probability\", name=\"Bad Credit\")\n",
    "# Third plot\n",
    "trace2 = go.Histogram(x=df_age, histnorm=\"probability\", name=\"Overall Age\")\n",
    "\n",
    "# Creating the grid\n",
    "fig = tls.make_subplots(\n",
    "    rows=2, cols=2, specs=[[{}, {}], [{\"colspan\": 2}, None]], subplot_titles=(\"Good\", \"Bad\", \"General Distribuition\")\n",
    ")\n",
    "\n",
    "# setting the figs\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "fig.append_trace(trace2, 2, 1)\n",
    "\n",
    "fig[\"layout\"].update(showlegend=True, title=\"Age Distribuition\", bargap=0.05)\n",
    "py.iplot(fig, filename=\"custom-sized-subplot-with-subplot-titles\")\n",
    "plot2 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e396a11e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0p/jtlt6ynn0l5bwr8p7p_1sq200000gn/T/ipykernel_60817/3132506002.py:7: UserWarning:\n",
      "\n",
      "\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "\n",
      "/var/folders/0p/jtlt6ynn0l5bwr8p7p_1sq200000gn/T/ipykernel_60817/3132506002.py:8: UserWarning:\n",
      "\n",
      "\n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_good = df_credit[df_credit[\"Risk\"] == \"good\"]\n",
    "df_bad = df_credit[df_credit[\"Risk\"] == \"bad\"]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(12, 8))\n",
    "plt.subplots_adjust(hspace=0.4, top=0.8)\n",
    "\n",
    "g1 = sns.distplot(df_good[\"Age\"], ax=ax[0], color=\"g\")\n",
    "g1 = sns.distplot(df_bad[\"Age\"], ax=ax[0], color=\"r\")\n",
    "g1.set_title(\"Age Distribuition\", fontsize=15)\n",
    "g1.set_xlabel(\"Age\")\n",
    "g1.set_xlabel(\"Frequency\")\n",
    "\n",
    "g2 = sns.countplot(x=\"Age\", data=df_credit, palette=\"hls\", ax=ax[1], hue=\"Risk\")\n",
    "g2.set_title(\"Age Counting by Risk\", fontsize=15)\n",
    "g2.set_xlabel(\"Age\")\n",
    "g2.set_xlabel(\"Count\")\n",
    "plt.show()\n",
    "plot3 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eea923d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's look the Credit Amount column\n",
    "interval = (18, 25, 35, 60, 120)\n",
    "\n",
    "cats = [\"Student\", \"Young\", \"Adult\", \"Senior\"]\n",
    "df_credit[\"Age_cat\"] = pd.cut(df_credit.Age, interval, labels=cats)\n",
    "\n",
    "\n",
    "df_good = df_credit[df_credit[\"Risk\"] == \"good\"]\n",
    "df_bad = df_credit[df_credit[\"Risk\"] == \"bad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d23dce05",
   "metadata": {},
   "outputs": [
    {
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       "config": {
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        "plotlyServerURL": "https://plot.ly",
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   "source": [
    "trace0 = go.Box(y=df_good[\"Credit amount\"], x=df_good[\"Age_cat\"], name=\"Good credit\", marker=dict(color=\"#3D9970\"))\n",
    "\n",
    "trace1 = go.Box(y=df_bad[\"Credit amount\"], x=df_bad[\"Age_cat\"], name=\"Bad credit\", marker=dict(color=\"#FF4136\"))\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(title=\"Credit Amount (US Dollar)\", zeroline=False), xaxis=dict(title=\"Age Categorical\"), boxmode=\"group\"\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"box-age-cat\")\n",
    "plot4 = fig"
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   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Housing\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Housing\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Housing\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Housing\"].value_counts().values,\n",
    "    name=\"Bad Credit\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(title=\"Housing Distribuition\")\n",
    "\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"Housing-Grouped\")\n",
    "plot5 = fig"
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "209c2632",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/songshuang/Desktop/dp-examples/.venv/lib/python3.12/site-packages/plotly/tools.py:455: DeprecationWarning:\n",
      "\n",
      "plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead\n",
      "\n"
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   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Sex\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Sex\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "\n",
    "# First plot 2\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Sex\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Sex\"].value_counts().values,\n",
    "    name=\"Bad Credit\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace2 = go.Box(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Sex\"],\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Credit amount\"],\n",
    "    name=trace0.name,\n",
    ")\n",
    "\n",
    "# Second plot 2\n",
    "trace3 = go.Box(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Sex\"],\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Credit amount\"],\n",
    "    name=trace1.name,\n",
    ")\n",
    "\n",
    "data = [trace0, trace1, trace2, trace3]\n",
    "\n",
    "\n",
    "fig = tls.make_subplots(rows=1, cols=2, subplot_titles=(\"Sex Count\", \"Credit Amount by Sex\"))\n",
    "\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 1, 2)\n",
    "fig.append_trace(trace3, 1, 2)\n",
    "\n",
    "fig[\"layout\"].update(height=400, width=800, title=\"Sex Distribuition\", boxmode=\"group\")\n",
    "py.iplot(fig, filename=\"sex-subplot\")\n",
    "\n",
    "plot6 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3cfdd7fc",
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   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Job\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Job\"].value_counts().values,\n",
    "    name=\"Good credit Distribuition\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Job\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Job\"].value_counts().values,\n",
    "    name=\"Bad Credit Distribuition\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(title=\"Job Distribuition\")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"grouped-bar\")\n",
    "\n",
    "plot7 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1779a072",
   "metadata": {},
   "outputs": [
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layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"yaxis\":{\"title\":{\"text\":\"Credit Amount distribuition by Job\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('c65e5040-6492-44bb-8af5-dcb8531ebbd3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace0 = go.Box(x=df_good[\"Job\"], y=df_good[\"Credit amount\"], name=\"Good credit\")\n",
    "\n",
    "trace1 = go.Box(x=df_bad[\"Job\"], y=df_bad[\"Credit amount\"], name=\"Bad credit\")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(yaxis=dict(title=\"Credit Amount distribuition by Job\"), boxmode=\"group\")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"box-age-cat\")\n",
    "\n",
    "plot8 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a66a1a65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12), nrows=2)\n",
    "\n",
    "g1 = sns.boxplot(x=\"Job\", y=\"Credit amount\", data=df_credit, palette=\"hls\", ax=ax[0], hue=\"Risk\")\n",
    "g1.set_title(\"Credit Amount by Job\", fontsize=15)\n",
    "g1.set_xlabel(\"Job Reference\", fontsize=12)\n",
    "g1.set_ylabel(\"Credit Amount\", fontsize=12)\n",
    "\n",
    "g2 = sns.violinplot(x=\"Job\", y=\"Age\", data=df_credit, ax=ax[1], hue=\"Risk\", split=True, palette=\"hls\")\n",
    "g2.set_title(\"Job Type reference x Age\", fontsize=15)\n",
    "g2.set_xlabel(\"Job Reference\", fontsize=12)\n",
    "g2.set_ylabel(\"Age\", fontsize=12)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4, top=0.9)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plot9 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07ecab56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "autobinx": false,
         "histnorm": "probability density",
         "legendgroup": "Good Credit",
         "marker": {
          "color": "rgb(31, 119, 180)"
         },
         "name": "Good Credit",
         "opacity": 0.7,
         "type": "histogram",
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