scam_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
    "os.environ[\"SPARK_HOME\"] = \"/home/ebrahim/Downloads/spark-3.0.1-bin-hadoop3.2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import findspark\n",
    "from pyspark.sql.session import SparkSession\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql import types as T\n",
    "from pyspark.sql import Window\n",
    "\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# findspark.init()\n",
    "spark = SparkSession.builder.master(\"local[*]\").getOrCreate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read all profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "profiles_df = spark.read.json('./cleaned/*.json').cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[age: string, children: string, description: string, drinking: string, email: string, ethnicity: string, gender: string, images: array<string>, inet: string, intent: string, justifications: array<string>, location: string, match_age: string, messages: array<string>, month_reported: string, name: string, occupation: string, orientation: string, phone: string, religion: string, scam: bigint, smoking: string, status: string, tags: array<string>, username: string, year_reported: string]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44263"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-----+\n",
      "|scam|count|\n",
      "+----+-----+\n",
      "|   0|36276|\n",
      "|   1| 7987|\n",
      "+----+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.groupBy(F.col(\"scam\")).count().show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dempographic analysis\n",
    "* Age\n",
    "* Gender\n",
    "* Ethnicity\n",
    "* Children\n",
    "* Marital status\n",
    "* Occupation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+\n",
      "|summary|               age|\n",
      "+-------+------------------+\n",
      "|  count|             42765|\n",
      "|   mean|41.435239097392724|\n",
      "| stddev| 11.08618575137918|\n",
      "|    min|                18|\n",
      "|    max|                88|\n",
      "+-------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.describe([\"age\"]).show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+\n",
      "|summary|               age|\n",
      "+-------+------------------+\n",
      "|  count|              6839|\n",
      "|   mean| 42.62143588243895|\n",
      "| stddev|11.146954365220273|\n",
      "|    min|                18|\n",
      "|    max|                78|\n",
      "+-------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.filter(~F.col(\"age\").isNull()).filter(\n",
    "    F.col(\"scam\") == 1\n",
    ").describe(\"age\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+\n",
      "|summary|               age|\n",
      "+-------+------------------+\n",
      "|  count|             35926|\n",
      "|   mean| 41.20943049601959|\n",
      "| stddev|11.060330846758077|\n",
      "|    min|                18|\n",
      "|    max|                88|\n",
      "+-------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.filter(~F.col(\"age\").isNull()).filter(\n",
    "    F.col(\"scam\") == 0\n",
    ").describe(\"age\").show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_pdf = profiles_df.filter(~F.col(\"age\").isNull()).withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").groupBy(F.col(\"age\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'(count, Real)'}, xlabel='age'>,\n",
       "       <AxesSubplot:title={'center':'(count, scam)'}, xlabel='age'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_pdf.set_index([\"age\", \"scam\"]).sort_index().unstack(level=1).plot(figsize=(20,10), subplots=True, kind=\"bar\", grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The age distribution looks interesting. Both have around the same mean however the distribution is different. For scammers it is clear we have to groups (27-36, 45-56)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_pdf = profiles_df.withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").groupBy(F.col(\"gender\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='(count, Real)'>,\n",
       "       <AxesSubplot:ylabel='(count, scam)'>], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_pdf.set_index([\"gender\", \"scam\"]).sort_index().unstack(level=1).plot(figsize=(20,10), subplots=True, kind=\"pie\", shadow=True, explode=(0, 0.05), autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age & gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_age_pdf = profiles_df.filter(~F.col(\"age\").isNull()).withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").groupBy(F.col(\"age\"), F.col(\"gender\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'(count, female, Real)'}, xlabel='age'>,\n",
       "       <AxesSubplot:title={'center':'(count, female, scam)'}, xlabel='age'>,\n",
       "       <AxesSubplot:title={'center':'(count, male, Real)'}, xlabel='age'>,\n",
       "       <AxesSubplot:title={'center':'(count, male, scam)'}, xlabel='age'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBDp27Mihhx7KLbfcwuOPP8706dNZsGABP/rRjwA49thjOfPMMznggAP47ne/y5133slee+3FL3/5SwYPHsy8efM+MIPmlFNOYdy4cRx11FFs3LiRn//859xwww0AbLnllvzyl7+kY8eOfO5zn+Phhx/+QOb555+v3vb3v/+dU089lS233LJGnQ8++CBXXXUVv/vd71i/fj2TJ0/m4Ycf5v333+fDH/4wv/vd75g3bx4XX3wxf/zjHz/w3G2//fa8/PLL1cuf+9znOProo9lvv/34xje+wSuvvMK7777LZz7zGSZPnsyf/vQnzjvvPHr16sUnPvEJFixYwJ133vmB5+uAAw7gj3/8I3369KnxvVu6dClr1tT8nXzMmDHzU0oj6/zmppSKugE/IDdjaBnwT+BtYDq5GUKdsn32A/6S3f8LsF92v1O2XzQ0xogRI1JDZs2a1eD2TTFv3nz5jd0e8x8+944at6tu+mON5bY+fmvXb37zyZdz7ebNm2+9fDnXbt58ffknn3yyoPybb75Z0vgN5efPn5++9KUvtcr4ixYtSt/4xjeKzpc6fiEuv/zydPvtt7fY2AsWLKjx/cjP1/V6AealenoxRZ96llI6DzgPoGpGUUrpuIj4HXAsMAOYAFR9NtzMbPnv2fb7s+IkFcDr1EiSJElqK4YPH86YMWNKnrVUjD333JMrrriiRcdsrJNPPrlZTvurz8qVK/n+97/fJMdqik89q+1cYEZEXAw8ClyXrb8OuDEilgKvA19ohrEl6QNsskmSJGlzk1Ii93lSrefEE09s1fH1b4ccckid64uZn9MkjaKU0mxgdnb/OWBUHftsADb9WXiSJEmSJKleXbt2ZdWqVfTp06fVm0Vqu1JKrFq1iq5duzYq1xwziiRps1LqjKTWntHU2uNLkiSpafXr14/ly5fz2muvNbhf1aeSFct88fm2UnvXrl3p169fo7I2iiRJkiRJKiOdO3dm4MCBm9xv9uzZDBs2rOhxzBefL+faOxQ9qiRJkiRJkjYrNookSZIkSZIEeOqZpALlX+fGa9xIkiRJ0ubJRpEkqU3zYtySJElSy/HUM0mSJEmSJAHOKJLaDWdlqL3ytS9JkiQVzkaRpDbP/+irnPn6lSRJUjnx1DNJkiRJkiQBNookSZIkSZKU8dQzSc3OU28kSZIkqTzYKJIkqQ2z0SpJkqSW5KlnkiRJkiRJApxRJElSg5zRI0mSpPbERpEkSWqTbNJJkiS1PE89kyRJkiRJElBCoygiukbE3Ih4LCKeiIiLsvUDI+LhiFgaETdHxBbZ+i7Z8tJs+4AmegySJEmSJElqAqWcevYv4KCU0rqI6Aw8FBF3Ad8ErkwpzYiInwEnAddmX99IKX0sIr4AXAZ8vsT6pbLhKRSSJEmSpLau6BlFKWddttg5uyXgIODWbP004Kjs/vhsmWz72IiIYseXJEmSJElS04qUUvHhiI7AfOBjwE+By4E5KaWPZdv7A3ellPaMiMeBcSml5dm2Z4F9Ukorax1zEjAJoG/fviNmzJhR7/jr1q2joqKi6PrNm29MftGKNTWW+3aDV9b/e3nwjj3bTb6cazdvvr3layun9972/NjNmzffdPlyrt28efPlm2/rtY8ZM2Z+SmlkXdtKahRVHySiF3Ab8B3g+lIaRflGjhyZ5s2bV++4s2fPZvTo0UXXbd58Y/J1nTr2o0X/PntzU6eObU75cq7dvPn2lq+tnN572/NjN2/efNPly7l28+bNl2++rdceEfU2iprkU89SSquBWcB+QK+IqPotrh+wIru/AuifFdQJ6AmsaorxJUmSJEmSVLpSPvVs22wmERHRDTgEWEyuYXRsttsE4Pbs/sxsmWz7/akppjNJkiRJkiSpSZTyqWfbA9Oy6xR1AG5JKd0REU8CMyLiYuBR4Lps/+uAGyNiKfA68IUSxpYkSZIkSVITK7pRlFJaCAyrY/1zwKg61m8APlvseJIkSZIkSWpeTXKNIkmSJEmSJJU/G0WSJEmSJEkCbBRJkiRJkiQpU8rFrCVJUhs3YPKfaiyfNfg9JuatWzblUy1dkiRJktowG0WSJGmzZJNMkiSp8Tz1TJIkSZIkSYAziiRJUgOclSNJktS+OKNIkiRJkiRJgDOKJElSM3JGkiRJUnlxRpEkSZIkSZIAG0WSJEmSJEnK2CiSJEmSJEkS4DWKVEZKvc6F18mQJEmSJKlhNorUYmzUSJLKiT+3JElSe+SpZ5IkSZIkSQKcUaRG8C+rkiRJkiRt3oqeURQR/SNiVkQ8GRFPRMSZ2freEXFPRCzJvm6drY+IuCoilkbEwogY3lQPQpIkSZIkSaUrZUbRe8BZKaUFEdEDmB8R9wATgftSSlMiYjIwGTgX+CQwKLvtA1ybfZUkSdrsOBNXkiSVo6JnFKWUXk4pLcjurwUWAzsC44Fp2W7TgKOy++OBG1LOHKBXRGxf7PiSJEmSJElqWpFSKv0gEQOAB4A9gRdSSr2y9QG8kVLqFRF3AFNSSg9l2+4Dzk0pzat1rEnAJIC+ffuOmDFjRr3jrlu3joqKiqLrNt+4/KIVa2os9+0Gr6z/9/LgHXuabyf5cq7dvHnz5ZMv59qbIl9bS//cLnV88+bbSr6cazdv3nz55tt67WPGjJmfUhpZ17aSG0URUQH8H3BJSukPEbG6qlGUbX8jpbR1oY2ifCNHjkzz5tW7mdmzZzN69OiiazffuHxdU+h/tOjfZy9uagq9+c0nX861mzdvvnzy5Vx7U+Rra+mf26WOb958W8mXc+3mzZsv33xbrz0i6m0UFX3qWXbgzsDvgekppT9kq1+pOqUs+/pqtn4F0D8v3i9bJ0mSJEmSpDaglE89C+A6YHFK6Yq8TTOBCdn9CcDteeuPzz79bF9gTUrp5WLHlyRJkiRJUtMq5VPP9ge+DCyKiMps3fnAFOCWiDgJeB74XLbtTuBwYCnwNnBCCWNLkiRJkiSpiRXdKMquNRT1bB5bx/4JOK3Y8SRJkiRJktS8SplRpBZW10UpJ+atK+aimo3JS5IkSZKkzVtJF7OWJEmSJEnS5sMZRZIkSfoAZyJLktQ+2SiSJElqg2zUSJKk1uCpZ5IkSZIkSQJsFEmSJEmSJCljo0iSJEmSJEmA1yiSJElSM/AaS5IklScbRZIkSWpzWrvR1NrjS5LUWjz1TJIkSZIkSYCNIkmSJEmSJGVsFEmSJEmSJAmwUSRJkiRJkqSMjSJJkiRJkiQBNookSZIkSZKU6dTaBbQnfsyqJElSy/D3LkmSilPSjKKImBoRr0bE43nrekfEPRGxJPu6dbY+IuKqiFgaEQsjYnipxUuSJEmSJKnplHrq2fXAuFrrJgP3pZQGAfdlywCfBAZlt0nAtSWOLUmSJEmSpCZU0qlnKaUHImJArdXjgdHZ/WnAbODcbP0NKaUEzImIXhGxfUrp5VJqkCRJktoaT32TJJWryPVtSjhArlF0R0ppz2x5dUqpV3Y/gDdSSr0i4g5gSkrpoWzbfcC5KaV5tY43idyMI/r27TtixowZ9Y69bt06Kioqiq69pfOLVqypsdy3G7yy/t/Lg3fsad58WeTLuXbz5s2XT76cazdvvtR8beX2e29bypdz7ebNmy/ffFuvfcyYMfNTSiPr2tasjaJs+Y2U0taFNoryjRw5Ms2bV+9mZs+ezejRo4uuvaXzdf1l6UeL/j2pa1N/WTJvvq3ky7l28+bNl0++nGs3b77UfG3l9ntvW8qXc+3mzZsv33xbrz0i6m0UlXqNorq8EhHbZwNvD7yarV8B9M/br1+2TpIkSZIkSW1AczSKZgITsvsTgNvz1h+fffrZvsAar08kSZIkSZLUdpR0MeuI+C25C1dvExHLgQuAKcAtEXES8DzwuWz3O4HDgaXA28AJpYwtSZIkSZKkplXqp559sZ5NY+vYNwGnlTJea/PTKyRJktQS/L1TktRamuPUM0mSJEmSJJUhG0WSJEmSJEkCSjz1TJIkSVLb09qnrrX2+JKk4rWrRpE/sCRJkqRN8/dmSWq/PPVMkiRJkiRJgI0iSZIkSZIkZWwUSZIkSZIkCbBRJEmSJEmSpIyNIkmSJEmSJAHt7FPPJEmSJLV9fuqaJLUeG0WSJEmSNiv5jSabTJLUOJ56JkmSJEmSJMAZRZIkSZJUzdPeJLV3ziiSJEmSJEkS4IwiSZIkSWozSp3R5IwoSaWyUSRJkiRJTcRGjaRy1+KnnkXEuIh4OiKWRsTklh5fkiRJkiRJdWvRGUUR0RH4KXAIsBx4JCJmppSebMk6JEmSJEkfVO6nvrX2+NLmoKVPPRsFLE0pPQcQETOA8YCNIkmSJElq51q70dPa40ttQUs3inYEXsxbXg7sU2jYf7SSJEmSpLaqtWdkmS/fnkFbeuyRUip451JFxLHAuJTSV7LlLwP7pJROz9tnEjApW9wFeLqBQ24DrCyhJPPmzZff2ObNm2+/+XKu3bx58753mDdvvn3l23rtH04pbVvnlpRSi92A/YC/5C2fB5xXwvHmlViPefPmy2xs8+bNt998Oddu3rx53zvMmzffvvLlXHtLf+rZI8CgiBgYEVsAXwBmtnANkiRJkiRJqkOLXqMopfReRJwO/AXoCExNKT3RkjVIkiRJkiSpbi19MWtSSncCdzbR4X5h3rz5VsmXc+3mzZsv33w5127evPnWy5dz7ebNmy/ffNnW3qIXs5YkSZIkSVLb1dLXKJIkSZIkSVIbZaNIkiRJkiRJgI0iSZLURCLiBxHx9dauozEiYpeIqIyItRHxtRYeO0XEx1pyzOYQEWdExGWtXYckSWoaXqNIkiSVLCK2BSqBj6WU1rdSDaOBm1JK/RqRuQ54M6X0jeaqq4GxEzAopbS0pcduShHRFVgKDE8pvdra9UiSpNI4o0iSJDWFicCdrdUkKsGHgSdau4hyllLaANwFHN/atUiSpNLZKJIkSU3hk8D/5a+IiPHZaV1vRsSzETEuW79DRMyMiNcjYmlE/Fde5vqIuDhveXRELM9bXhYR34qIhRGxJiJujoiuEbEluWbFDhGxLrvt0FDBEXE/MAa4Jtt/54joEhE/jIgXIuKViPhZRHTLryUizomIVyPi5Yg4KiIOj4hnssdzft7xR0XE3yNidbbvNRGxRT211DvupmTjP5mdPrciIr5VwPfghIhYnGWei4iTaz/nhT7OzGzgU4XUK0mS2jYbRZIkqSkMBp6uWoiIUcANwNlAL+ATwLJs8wxgObADcCxwaUQc1IixPgeMAwYCQ4CJKaW3yDWrXkopVWS3lxo6SErpIOBB4PRs/2eAKcDOwFDgY8COwHfzYh8Cuuat/yXwJWAEcADwnYgYmO27EfgGsA2wHzAWOLWecjY1bkOuA05OKfUA9gTuh01+D14FjgC2Ak4AroyI4UU+ToDFwF4F1itJktowG0WSJKkp9ALW5i2fBExNKd2TUno/pbQipfRURPQH9gfOTSltSClVAr+icactXZVSeiml9Drwv+SaKyWLiAAmAd9IKb2eUloLXAp8IW+3d4FLUkrvkmt4bQP8JKW0NqX0BPAkWcMkpTQ/pTQnpfReSmkZ8HPgwCLHbci7wO4RsVVK6Y2U0oJsfZ3fg6y2P6WUnk05/wfcTa4B1OjHmVkL9CywXkmS1IbZKJIkSU3hDaBH3nJ/4Nk69tsBqGqGVHme3MyVQv0z7/7bQEUjsg3ZFugOzM9OF1sN/DlbX2VVSmljdr/qekyv5G1fX1VPdirbHRHxz4h4k1zzZ5six23IZ4DDgecj4v8iYr9sfX3fAyLikxExJzuNbHWWz6+t4MeZ6QGsKbBeSZLUhtkokiRJTWEhuVOnqrwIfLSO/V4CekdEflNpJ2BFdv8tck2TKh9qRA2lfpTrSnINkD1SSr2yW8+UUrGNqGuBp8h9stlWwPlANPW4KaVHUkrjge2APwK3ZJvq/B5ERBfg98APgb4ppV7AnfXUVqjdgMdKyEuSpDbCRpEkSWoKd1LztKrrgBMiYmxEdIiIHSNi15TSi8DfgB9kF6EeQu4UqZuyXCVweET0jogPAV9vRA2vAH0iovoUqOzCzAU1kFJK75O7Fs+VEbFdlt8xIg5rRA35egBvAusiYlfgq8WOGxEpIkbXzkbEFhFxXET0zE4TexN4P9tc5/cA2ALoArwGvBcRnwQOLfIxVjmQ3MXEJUlSmbNRJEmSmsIN5Bo83QBSSnPJLpJM7pSk/yP3UfQAXwQGkJtddBtwQUrp3mzbjeRmpiwjd92cmwstILv+zm+B57JTuHYgd/rV3xrxOM4FlgJzstPF7gV2aUQ+37eA/yR3/Z5f0vBjqXfc7LpOa4FF9WS/DCzLcqcAx0H934PstL+vkZt59EZW48wiHyMR0ZXcqWvTij2GJElqOyKlUmdpS5IkQURcCryaUvpxa9dSJSJ+BfwupfSX1q6lWBHxJXKnpZ3X2rXUJSLOAPqnlM5p7VokSVLpbBRJkiRJkiQJ8NQzSZIkSZIkZWwUSZIkSZIkCbBRJEmSJEmSpEyn1i6gIdtss00aMGBAvdvfeustttxyy6KPb968+eLy5Vy7efPmyzdfzrWbN2/e9w7z5s23r3xbr33+/PkrU0rb1rkxpdRmbyNGjEgNmTVrVoPbN8W8efPlN7Z58+bbb76cazdv3nzr5cu5dvPmzZdvvq3XDsxL9fRiPPVMkiRJkiRJgNcokiRJkiRJUsZGkSRJkiRJkoA2fjFrSZIkSZLas3fffZfly5ezYcOGRmd79uzJ4sWLix7bfPH5tlJ7165d6devH507dy44a6NIKhcX9qy5vMtFcOH4vO1rWrYeSZIkSc1u+fLl9OjRgwEDBhARjcquXbuWHj16FD22+eLzbaH2iooKVq1axfLlyxk4cGDBWU89kyRJkiSpjdqwYQN9+vRpdJNIigj69OnT6NloNookSZIkSWrDbBKpWMW8dmwUSZIkSZIkCfAaRZIkSZIklY/a1y5tQEFXuPFap6rFGUWSJEmSJKle69ev58ADD2Tjxo0tMt6ll15a0H4PPvgge+yxB0OHDmX9+vXNUsvs2bM54ogjmuXYTemaa65h6tSpTXIsG0WSJEmSJKleU6dO5ZhjjqFjx44tMl6hjaLp06dz3nnnUVlZSbdu3Zq5qrbtxBNP5Oqrr26SY9kokiRJkiRJ9Zo+fTrjx4+vXr7ssssYPHgwe+21F5MnTwagsrKSfffdlyFDhnD00UfzxhtvADB69GjmzZsHwMqVKxkwYAAA119/Pccccwzjxo1j0KBBnHPOOQBMnjyZ9evXM3ToUE466aR6a/rVr37FLbfcwne+8x2OO+44AC6//HL23ntvhgwZwgUXXADAsmXL2HXXXZk4cSI777wzxx13HPfeey/7778/gwYNYu7cuQDMnTuX/fbbj2HDhvEf//EfPP300x8Y86233uLEE09k1KhRDBs2jNtvv73B5+2tt97iU5/6FHvttRd77rknN998MwCPPPII//Ef/8Fee+3FqFGjWLt2LcuWLeOAAw5g+PDhDB8+nIcffhjIzWg68MADGT9+PB/5yEeYPHky06dPZ9SoUQwePJhnn30WgO7duzNgwIDqx1MKr1EkSZIkSZLq9M477/Dcc89VN3juuusubr/9dh5++GG6d+/O66+/DsDxxx/P1VdfzYEHHsh3v/tdLrroIr7//e83eOzKykoeffRRunTpwi677MIZZ5zBlClTuOaaa6isrGTt2rX1Zr/yla/w0EMPccQRR3Dsscdy9913s2TJEubOnUtKiSOPPJK//vWv7LrrrixdupTf/e53TJ06lb333pvf/OY3PPTQQ8ycOZNLL72UP/7xj+y66648+OCDdOrUiXvvvZfzzz+f66+/vsaYl1xyCQcddBBTp05l9erVjBo1ioMPPpgtt9yyzhr//Oc/s8MOO/CnP/0JgDVr1vDOO+/w+c9/nptvvpm9996bN998k27durHddttxzz330LVrV5YsWcLnP/95FixYAMBjjz3G4sWL6d27Nx/5yEf4yle+wty5c/nJT37C1VdfzY9//GMARo4cyYMPPsioUaM29W1tkI0iSZIkSZJUp5UrV9KrV6/q5XvvvZcTTjiB7t27A9C7d2/WrFnD6tWrOfDAAwGYMGECn/3sZzd57LFjx9KzZ+7i3LvvvjvPP/88/fv3L6rOu+++m7vvvpthw4YBsG7dOp599ll23XVXBg4cyODBgwHYY489GDt2LBHB4MGDWbZsGZBr4kyYMIElS5YQEbz77rt1jjFz5kx++MMfArBhwwZeeOEFdttttzprGjx4MGeddRbnnnsuRxxxBAcccACLFi1i++23Z++99wZgq622AnKzj04//XQqKyvp2LEjzzzzTPVx9t57b7bffnsAPvrRj3LooYdWH3/WrFnV+2233XY89dRTRT1/+WwUSZIkSZKkOnXr1o0NGzYUne/UqRPvv/8+wAeO06VLl+r7HTt25L333it6nJQS5513HieffHL1urVr17Jq1aoa43To0KF6uUOHDtVjfuc732HMmDHcdtttLFu2jNGjR9c5xu9//3t22WWXgmraeeedWbBgAXfeeSff/va3GTt2LEcffXSd+1555ZX07duXxx57jPfff5+uXbtWbyukfsg9v01xrSYbRZIkSZIklYtGfJz92rVr6dGjR0nDbb311mzcuJENGzbQtWtXDjnkEL73ve9x3HHHVZ961rt3b7beemsefPBBDjjgAG688cbq2UUDBgxg/vz5jBo1iltvvbWgMTt37lxjRs/YsWO54YYb2HHHHevNHHbYYdXXK6qoqGDFihX861//okOHwi7NvGbNmurj1z7lLH+Mq6++mquvvpqI4NFHH2XYsGGsWLGC448/nvvuu6/G/i+99BK9e/fmS1/6Er169eJXv/oVkydP5uWXX+aRRx5h7733Zu3atXTr1o01a9bQr18/OnTowLRp04r6hLlnnnmG/fffv9G52ryYtSRJkiRJqtehhx7KQw89BMC4ceM48sgjGTlyJEOHDq0+DWvatGmcffbZDBkyhMrKSr773e8C8K1vfYtrr72WYcOGsXLlyoLGmzRpEkOGDOGkk07i/fffZ+nSpfTu3XuTNf7nf/4n++23H4MHD+bYY49t8BpHtZ1zzjmcd955DBs2rN6ZTd/5znd49913GTJkCHvssQff+c53AHj55Zfp1OmD83AWLVrEqFGjGDp0KBdddBHf/va32WKLLbj55ps544wz2GuvvTjkkEPYsGEDp556KtOmTWOvvfbiqaeeqve6Rw3561//yiGHHNLoXG3OKJIkSZIkSfU67bTTuPLKKzn44IOB3CeTVX3aWZWhQ4cyZ86cGuvWrl3LrrvuysKFC6vXXXzxxQBMnDiRiRMnVq+/4447qu9fdtllXHbZZaxdu5Ynn3ySz3zmM3WeUlV75s+ZZ57JmWeeWWP8Hj168Pjjj9eZGTBgQPW2/fbbr8Z1gS6++GLWrl3L6NGjq09D69atGz//+c8/UMecOXM47bTTPrD+sMMO47DDDvvA+r333vsDz9WgQYNqPE/f/va3AWqMD7lPQauSv+3RRx9ljz32oE+fPh8Yr7FsFEmSJEmSpHoNHz6cMWPGsHHjRjp27NiiY++5555cccUVLTpmY51++umtXQIrV67c5KfMFcpGkSRJkiRJbVhKiYho1RpOPPHEVh1fDavvlLOUUqOP5TWKJEmSJElqo7p27cqqVauK+g+/2reUEqtWrarxCWqFcEaRJEmSJEltVL9+/Vi+fDmvvfZao7NVn1RWLPPF59tK7V27dqVfv36NytookiRJkiSpjercuTMDBw4sKjt79myGDRtW9Njmi8+Xc+2eeiZJkiRJkiTARpEkSZIkSZIyNookSZIkSZIE2CiSJEmSJElSxotZSyrMhT3/fX+Xi+DC8Xnb1rR8PZIkSZKkJueMIkmSJEmSJAE2iiRJkiRJkpQpulEUEf0jYlZEPBkRT0TEmdn63hFxT0Qsyb5una2PiLgqIpZGxMKIGN5UD0KSJEmSJEmlK+UaRe8BZ6WUFkRED2B+RNwDTATuSylNiYjJwGTgXOCTwKDstg9wbfZVah/yr/EDXudHkiRJktTmFD2jKKX0ckppQXZ/LbAY2BEYD0zLdpsGHJXdHw/ckHLmAL0iYvtix5ckSZIkSVLTipRS6QeJGAA8AOwJvJBS6pWtD+CNlFKviLgDmJJSeijbdh9wbkppXq1jTQImAfTt23fEjBkz6h133bp1VFRUFF23efMtmn+5sma+yw5U/Oulf6/Yfmjz5msppf4WH9u8efPmW3ls8+bNl2++nGs3b958+ebbeu1jxoyZn1IaWefGlFJJN6ACmA8cky2vrrX9jezrHcDH89bfB4xs6NgjRoxIDZk1a1aD2zfFvPkWzV+wVY3brN9cWXNdGeUbna2l7L535s2bbxP5cq7dvHnzrZcv59rNmzdfvvm2XjswL9XTiynpU88iojPwe2B6SukP2epXqk4py76+mq1fAfTPi/fL1kmSJEmSJKkNKOVTzwK4DlicUroib9NMYEJ2fwJwe97647NPP9sXWJNSernY8SVJkiRJktS0SvnUs/2BLwOLIqIyW3c+MAW4JSJOAp4HPpdtuxM4HFgKvA2cUMLYkiRJkiRJamJFN4pS7qLUUc/msXXsn4DTih1PkiRJkiRJzaukaxRJkiRJkiRp82GjSJIkSZIkSYCNIkmSJEmSJGVsFEmSJEmSJAmwUSRJkiRJkqRM0Z96JkkFu7BnzeVdLoILx+dtX9O8eUmSJElSQZxRJEmSJEmSJMBGkSRJkiRJkjI2iiRJkiRJkgR4jSKpcF4nR5IkSZK0mXNGkSRJkiRJkgAbRZIkSZIkScrYKJIkSZIkSRLgNYoktQdeX0qSJEmSCmKjSO2HzQJJkiRJkhpko0gtx0aNJEmSJEltmtcokiRJkiRJEmCjSJIkSZIkSRkbRZIkSZIkSQJsFEmSJEmSJCljo0iSJEmSJEmAjSJJkiRJkiRlbBRJkiRJkiQJsFEkSZIkSZKkjI0iSZIkSZIkATaKJEmSJEmSlOnU2gVIBbuwZ83lXS6CC8fnbV/TsvWo/fC1J0mSJKmdcEaRJEmSJEmSABtFkiRJkiRJytgokiRJkiRJEmCjSJIkSZIkSRkbRZIkSZIkSQJKbBRFxNSIeDUiHs9b1zsi7omIJdnXrbP1ERFXRcTSiFgYEcNLLV6SJEmSJElNp9QZRdcD42qtmwzcl1IaBNyXLQN8EhiU3SYB15Y4tiRJkiRJkppQSY2ilNIDwOu1Vo8HpmX3pwFH5a2/IeXMAXpFxPaljC9JkiRJkqSm0xzXKOqbUno5u/9PoG92f0fgxbz9lmfrJEmSJEmS1AZESqm0A0QMAO5IKe2ZLa9OKfXK2/5GSmnriLgDmJJSeihbfx9wbkppXq3jTSJ3ahp9+/YdMWPGjHrHXrduHRUVFUXXbr6F8y9X1sx32YGKf7307xXbDzVfJvlyrr1V8rWU3b9d8+bbSL6cazdv3rzvHebNm29f+bZe+5gxY+anlEbWuTGlVNINGAA8nrf8NLB9dn974Ons/s+BL9a1X323ESNGpIbMmjWrwe2bYr6F8xdsVeM26zdX1lxnvmzy5Vx7q+RrKbt/u+bNt5F8Oddu3rz51suXc+3mzZsv33xbrx2Yl+rpxTTHqWczgQnZ/QnA7Xnrj88+/WxfYE369ylqkiRJkiRJamWdSglHxG+B0cA2EbEcuACYAtwSEScBzwOfy3a/EzgcWAq8DZxQytiSJEmSJElqWiU1ilJKX6xn09g69k3AaaWMJ0mSJEmSpObTHKeeSZIkSZIkqQzZKJIkSZIkSRJgo0iSJEmSJEkZG0WSJEmSJEkCSryYtdqZC3vWXN7lIrhwfN72NS1bjyRJkiRJalLOKJIkSZIkSRJgo0iSJEmSJEkZG0WSJEmSJEkCbBRJkiRJkiQp48WsJam5eSF4SZIkSWXCRpEktXU2miRJkiS1EE89kyRJkiRJEuCMovbFWQmSJEmSJKkBziiSJEmSJEkSYKNIkiRJkiRJGRtFkiRJkiRJArxGkSRt/rw+mSRJkqQCOaNIkiRJkiRJgDOKJEnNzRlNkiRJUtlwRpEkSZIkSZIAG0WSJEmSJEnKeOpZOfH0DUmSJEmS1IxsFEmSGmaTWpIkSWo3PPVMkiRJkiRJgI0iSZIkSZIkZWwUSZIkSZIkCbBRJEmSJEmSpIwXs5YktW1eTFuSJElqMTaKJElqTja6JEmSVEZsFLUk/7MgSWosf3ZIkiSpBXmNIkmSJEmSJAHOKJIkbe6ckSMVx387kiS1SzaKGsNfmCRJ7Y0/+yRJktqVFm8URcQ44CdAR+BXKaUpBYdL/WXVX3YlSVJ74e89kiSpCC3aKIqIjsBPgUOA5cAjETEzpfRkS9YhSVLByv0/261df2uOX+5/YGrt8Vtbe3/8kiS1kpaeUTQKWJpSeg4gImYA4wEbRZIkqaZybxSUe/1qXb5+JEmtJFJKLTdYxLHAuJTSV7LlLwP7pJROz9tnEjApW9wFeLqBQ24DrCyhJPPmzZff2ObNm2+/+XKu3bx58753mDdvvn3l23rtH04pbVvnlpRSi92AY8ldl6hq+cvANSUcb16J9Zg3b77MxjZv3nz7zZdz7ebNm/e9w7x58+0rX861d6BlrQD65y33y9ZJkiRJkiSplbV0o+gRYFBEDIyILYAvADNbuAZJkiRJkiTVoUUvZp1Sei8iTgf+AnQEpqaUnijhkL8osSTz5s2X39jmzZtvv/lyrt28efOtly/n2s2bN1+++bKtvUUvZi1JkiRJkqS2q6VPPZMkSZIkSVIbZaNIkiRJkiRJgI0iSZIkSZIkZWwUSZKkFhMRP4iIr7d2Ha0lIq6PiItbu46G5NcYEUMi4m+tXZMkSWo5NookSVKLiIhtgeOBn7diDaMjYnlrjd9UssfxfkSsi4i1EfF0RJzQ1OOklBYCqyPi0019bEmS1DbZKJIkSS1lInBnSml9axeymXgppVQBbAV8A/hlROzSDONMB05uhuNKkqQ2yEaRJElqKZ8E/i9/RUSMj4jKiHgzIp6NiHHZ+h0iYmZEvB4RSyPiv/IyNU7fqj1LKCKWRcS3ImJhRKyJiJsjomtEbAncBeyQzcRZFxE7bKro7HhnZ8d7KyKui4i+EXFXNpvn3ojYOm//30XEP7OxH4iIPRo49hHZ418dEX+LiCEFPpfVUs6dwOvAkOy4HSJicvacroqIWyKidzE1ArOBsRHRpbG1SZKk8mOjSJIktZTBwNNVCxExCrgBOBvoBXwCWJZtngEsB3YAjgUujYiDGjHW54BxwEByzZOJKaW3yDWrXkopVWS3lwo83meAQ4CdgU+TazidD2xL7vepr+XtexcwCNgOWEBuRs4HRMQwYCq52Tp9yJ2SN7OxDZmsKXQksA2wNFt9BnAUcCC55/AN4KeNrREgpbQCeBdojtlKkiSpjenU2gVIkqR2oxewNm/5JGBqSumebHkFQET0B/YHPpVS2gBURsSvyF3f6P4Cx7qqqgkUEf8LDC2x9qtTSq9kx3sQeDWl9Gi2fBswtmrHlNLUqvsRcSHwRkT0TCmtqXXMScDPU0oPZ8vTIuJ8YF9qzbyqxw4RsRroRu53um9W1QScApyeUlqeV8cLEfHllNJ7jaixylpy3z9JkrSZc0aRJElqKW8APfKW+wPP1rHfDsDrKaX8ptLzwI6NGOufefffBioaka3LK3n319exXAEQER0jYkp2yteb/HuG1DZ1HPPDwFnZaWers6ZPf3KPvxAvpZR6kbtG0VVA/oyrDwO35R13MbAR6NvIGqv0AFYXWJckSSpjNookSVJLWUju1K0qLwIfrWO/l4DeEZHfVNqJbMYR8BbQPW/bhxpRQ2rEvsX4T2A8cDDQExiQrY869n0RuCSl1Cvv1j2l9NvGDJhS+hdwLjA4Io7KO/Ynax27a3YaWWNqJCJ2BLYg77RBSZK0+bJRJEmSWsqd5K6ZU+U64ISIGJtdZ2fHiNg1pfQi8DfgB9lFqIeQO03tpixXCRweEb0j4kPA1xtRwytAn4joWbUiuxh2UzWQegD/AlaRa2Zd2sC+vwROiYh9ImfLiPhUVYMsu2j39YUMmlJ6B/gR8N1s1c+ASyLiw9mxto2I8UXUCLnv2f1ZQ0qSJG3mbBRJkqSWcgO5Bk83gJTSXOAE4EpgDbnr8nw42/eL5Ga6vATcBlyQUro323Yj8Bi5U6buBm4utICU0lPAb4HnstOydiB3utffSnlgeW4gd5rcCuBJYE4DtcwD/gu4htxpeUuBiXm79Af+2oixpwI7RcSngZ8AM4G7I2JtVsc+ja0xcxy5xpMkSWoHIqXmnoEtSZKUExGXkrsQ9I9bu5Yq2YWyf5dS+ktr11IlIrYg1wwbklJ6txXrGELugtv7tVYNkiSpZdkokiRJkiRJEuCpZ5IkSZIkScrYKJIkSZIkSRJgo0iSJEmSJEmZTq1dQEO22WabNGDAgHq3v/XWW2y55ZZFH9+8efPF5cu5dvPmzZdvvpxrN2/evO8d5s2bb1/5tl77/PnzV6aUtq1zY0qpzd5GjBiRGjJr1qwGt2+KefPmy29s8+bNt998Oddu3rz51suXc+3mzZsv33xbrx2Yl+rpxXjqmSRJkiRJkgCvUSRJkiRJkqSMjSJJkiRJkiQBBVzMOiKmAkcAr6aU9szW9QZuBgYAy4DPpZTeiIgAfgIcDrwNTEwpLcgyE4BvZ4e9OKU0rWkfiiRJkiRJqhIR/OMf/2DDhg1F5Xv27MnixYuLHr8959tK7V27dqVfv3507ty54Gwhn3p2PXANcEPeusnAfSmlKRExOVs+F/gkMCi77QNcC+yTNZYuAEYCCZgfETNTSm8UXKkkqVUMnja4xvJXK77KGdPOqF5eNGFRS5ckSZKkAmy55Zb06NGDAQMGkJvX0Thr166lR48eRY/fnvNtofaKigpWrVrF8uXLGThwYMHZTZ56llJ6AHi91urxQNWMoGnAUXnrb8guoj0H6BUR2wOHAfeklF7PmkP3AOMKrlKSJEmSJDVKx44d6dOnT1FNIpW/iKBPnz6NnlFW7DWK+qaUXs7u/xPom93fEXgxb7/l2br61kuSJEmSpGZik6h9K+b7HymlQg48ALgj7xpFq1NKvfK2v5FS2joi7gCmpJQeytbfR+6UtNFA15TSxdn67wDrU0o/rGOsScAkgL59+46YMWNGvXWtW7eOioqKwh6pefPmmyxfzrWbb3z+yVVP1ljetuO2vLbxterl3fvs3qzjmzffFsY2b958+ebLuXbz5kvNb7XVVgwaNKjo/MaNG+nYsaP5Mhu7dn7p0qWsWbOmxvYxY8bMTymNrDOcUtrkjdxFqx/PW34a2D67vz3wdHb/58AXa+8HfBH4ed76GvvVdxsxYkRqyKxZsxrcvinmzZdTfs/r96xx++mtP62x3NzjN1XWfPnl29Jrz3z7zpdz7ebNm2+9fDnXbt58qfkFCxbUWK79e12pt015880309tvv50+8YlPpPfee6/R9b/55puNzlxyySUl5Qsdf8sttyw6e+CBB6add945DRkyJI0cOTI9+uijjcpX+cc//pH22GOPlFJKCxcuTBMmTKgz/+STT34gC8xL9fRiij31bCYwIbs/Abg9b/3xkbMvsCblTlH7C3BoRGwdEVsDh2brJEmSJEnSZmrq1Kkcc8wxJc2OaYxLL720RcYp1fTp03nsscc49dRTOfvss0s+3uDBg1m+fDkvvPBCycfaZKMoIn4L/B3YJSKWR8RJwBTgkIhYAhycLQPcCTwHLAV+CZwKkFJ6Hfg+8Eh2+162TpIkSZIkbaamT5/O+PHjq5cvu+wyBg8ezF577cXkyZMBqKysZN9992XIkCEcffTRvPFG7gPSDz/8cObNmwfAypUrGTBgAADXX389xxxzDOPGjWPQoEGcc845AEyePJn169czdOhQjjvuuAbrGjBgAOeddx5Dhw5l5MiRLFiwgMMOO4yPfvSj/OxnPwNyp/6NHTuW4cOHM3jwYG6//fY6j3X55Zez9957M2TIEC644IJGPT/77bcfK1asAOCtt97ixBNPZNSoUXz84x+vHm/ZsmUccMABDB8+nOHDh/O3v/2tzmN9+tOfpqHL9xSq06Z2SCl9sZ5NY+vYNwGn1XOcqcDURlUnSZIkSZLK0jvvvMNzzz1X3eC56667uP3223n44Yfp3r07r7+emz9y/PHHc/XVV3PggQfy3e9+l4suuogf//jHDR67srKSRx99lC5durDLLrtwxhlnMGXKFK655hoqKyuB3EfEN2SnnXaisrKSb3zjG0ycOJG//vWvbNiwgT333JNTTjmFrl27ctttt7HVVluxcuVK9t13X4488sgaF4i+++67WbJkCXPnziWlxJFHHskDDzzAsGHDCnqO/vznP3PUUUcBcMkll3DQQQcxdepUXnzxRcaOHcvBBx/Mdtttxz333EPXrl1ZsmQJX/ziF6sbaPlGjhzJlClTqhtnxdpko0iSJEmSJKmxVq1aRa9evaqX7733Xk444QS6d+8OQO/evVmzZg2rV6/mwAMPBGDChAl89rOf3eSxx44dS8+ePQHYfffdef755+nfv3+j6jvyyCOB3Glb69ato0ePHvTo0YMuXbqwevVqUkqcf/75PPDAA3To0IEVK1bwyiuv8KEPfaj6GHfffTd33313dWNo3bp1LFmyZJONouOOO4533nmHdevWVTe27r77bmbOnMkPf/hD3n//fTZs2MALL7zADjvswOmnn05lZSUdO3bkmWeeqfOY2223HS+99FKjnoO62CiSJEmSJElNrmvXrmzYsKHofKdOnXj//fcBPnCcLl26VN/v2LEj7733XqOPX3WMDh061Dhehw4deO+997j11lt57bXXmD9/Pp07d2bAgAEfqCOlxHnnncfJJ59cY/2mZjNNnz6dESNGcPbZZ3PGGWfwhz/8gZQSv//979lll11Yu3YtPXr0AODCCy+kb9++PPbYY7z//vt07dq1zmNu2LCBbt26Nfp5qK3Yi1lLkiRJkiTVa+utt2bjxo3VzZVDDjmEX//617z99tsAvP766/Ts2ZOtt96aBx98EIAbb7yxenbRTjvtxPz58wG49dZbCxqzc+fOvPvuu9XLY8eOrb4GUGOtWbOG7bbbjs6dOzNr1iyef/75D+xz2GGHMXXqVNatWwfAihUrePXVVwsaOyL4/ve/z5w5c3jqqac47LDDuPrqq6s+LZ5HH320uo7tt9+eDh06cOONN7Jx48Y6j/fMM8+w5557FvVY8zmjSJIkSZKkdmDRhEWN2j9/VkuxDj30UB566CEOPvhgxo0bR2VlJSNHjmSLLbbg8MMP59JLL2XatGmccsopvP3223zkIx/h17/+NQBf+9rXOOGEE/jFL37Bpz71qYLGmzRpEkOGDGH48OH8z//8D0uXLqV3795F1f75z3+eL37xiwwePJiRI0ey66671vn4Fi9ezH777QdARUUFN910E126dClo7G7dunHWWWdx+eWXc8011/D1r3+dIUOG8N577/HRj36UO+64g1NPPZXPfOYz3HDDDYwbN44tt9yyzmPNmjWr4OepITaKJG32Bk8bXGP5qxVf5YxpZ1QvN/YHpiRJkqTCnHbaaVx55ZUcfPDBQO6Tyao+7azK0KFDmTNnzgeyO++8MwsXLqxevvjiiwGYOHEiEydOrF5/xx13VN+/7LLLuOyyywB4+OGH+cxnPlPn6VjLli2rvl/7eFXbunTpwt///vc6H1fVDCKAM888kzPPPLPG9obGnj17do3ls846q/r+z3/+c6Bmk27QoEE1noeqxzdgwAAef/xxAP71r38xb968TV4EvBCeeiZJkiRJkprF8OHDGTNmTL2nSzWn3XffnSuuuKLFx22NsV944QWmTJlCp06lzwdyRpEkSZIkSWo2J554YmuXsNkbNGgQgwYNapJjOaNIkiRJkqTNVNWFkdU+FfP9t1EkSZIkSdJmaOPGjaxatcpmUTuVUmLVqlV07dq1UTlPPZMkSZIkaTP01ltvsXbtWl577bWi8hs2bGh0k8F864+dn+/atSv9+vVrVNZGkSRJkiRJm6GUEgMHDiw6P3v2bIYNG2a+zMYuNW+jSGon/Ih4SZIkSdKm2CiSpDbOJp8kSZKkluLFrCVJkiRJkgQ4o0hSgfJntbT0jBZn1EiSJElSy3BGkSRJkiRJkgBnFElqAeU+I6jc65ckSZKkQjmjSJIkSZIkSYAziqQW46wUSZIkSVJbV3SjKCJ2AW7OW/UR4LtAL+C/gNey9eenlO7MMucBJwEbga+llP5S7PiSVC5sEkqSJEkqF0U3ilJKTwNDASKiI7ACuA04AbgypfTD/P0jYnfgC8AewA7AvRGxc0ppY7E1SJIkSZIkqek01TWKxgLPppSeb2Cf8cCMlNK/Ukr/AJYCo5pofEmSJEmSJJUoUkqlHyRiKrAgpXRNRFwITATeBOYBZ6WU3oiIa4A5KaWbssx1wF0ppVtrHWsSMAmgb9++I2bMmFHvuOvWraOioqLous2bb0z+yVVP1ljetuO2vLbxterl3fvs3m7y5Vx7e8zXVm6v/drK7b3DfNPly7l28+bN+95h3rz59pVv67WPGTNmfkppZF3bSm4URcQWwEvAHimlVyKiL7ASSMD3ge1TSicW2ijKN3LkyDRv3rx6x549ezajR48uunbz5huTr+s6M9euu7Z6eVPXmdmc8uVce3vM11Zur/3ayu29w3zT5cu5dvPmzfveYd68+faVb+u1R0S9jaKmOPXsk+RmE70CkFJ6JaW0MaX0PvBL/n162Qqgf16uX7ZOkiRJkiRJbUDRF7PO80Xgt1ULEbF9SunlbPFo4PHs/kzgNxFxBbmLWQ8C5jbB+JIktVl+6p0kSZLKSUmNoojYEjgEODlv9X9HxFByp54tq9qWUnoiIm4BngTeA07zE88kSc3NRo0kSZJUuJIaRSmlt4A+tdZ9uYH9LwEuKWVMSVLjlHujpNzrlyRJkspJU1yjSJIkSZIkSZsBG0WSJEmSJEkCbBRJkiRJkiQpY6NIkiRJkiRJQIkXs5YkaXPX2hfTbu3xJUmS1L44o0iSJEmSJEmAjSJJkiRJkiRlPPVMkiS1SZ52J0mS1PKcUSRJkiRJkiTARpEkSZIkSZIyNookSZIkSZIEeI0itSNe60JSe+R7nyRJkhrDGUWSJEmSJEkCnFEkSZIa4IwkSZKk9sUZRZIkSZIkSQJsFEmSJEmSJCljo0iSJEmSJEmAjSJJkiRJkiRlSmoURcSyiFgUEZURMS9b1zsi7omIJdnXrbP1ERFXRcTSiFgYEcOb4gFIkiRJkiSpaTTFjKIxKaWhKaWR2fJk4L6U0iDgvmwZ4JPAoOw2Cbi2CcaWJEmSJElSE2mOU8/GA9Oy+9OAo/LW35By5gC9ImL7ZhhfkiRJkiRJRYiUUvHhiH8AbwAJ+HlK6RcRsTql1CvbHsAbKaVeEXEHMCWl9FC27T7g3JTSvFrHnERuxhF9+/YdMWPGjHrHX7duHRUVFUXXb7595Z9c9WSN5W07bstrG1+rXt69z+7mC8yXc+3mzZsvn3ypY9dWbj+3zJs33zT5cq7dvHnz5Ztv67WPGTNmft6ZYTWU2ijaMaW0IiK2A+4BzgBmVjWKsn3eSCltXWijKN/IkSPTvHn1bmb27NmMHj266PrNt6/84GmDayx/teKrXLvu32dALpqwyHyB+XKu3bx58+WTb+3aayu3n3vmzZtv/bHNmzfffvNtvfaIqLdRVNKpZymlFdnXV4HbgFHAK1WnlGVfX812XwH0z4v3y9ZJkiRJkiSpDehUbDAitgQ6pJTWZvcPBb4HzAQmAFOyr7dnkZnA6RExA9gHWJNSermU4iVJkppLXTOSzph2RvVyY2ckSZIklYOiG0VAX+C23GWI6AT8JqX054h4BLglIk4Cngc+l+1/J3A4sBR4GzihhLElSZIkSZLUxIpuFKWUngP2qmP9KmBsHesTcFqx40mSJEmSJKl5lXSNIkmSJEmSJG0+Sjn1TGpRXitCkiRJkqTm5YwiSZIkSZIkAc4okiRJahbOhJUkSeXIGUWSJEmSJEkCnFEkSZK0WXJGkyRJKoYziiRJkiRJkgTYKJIkSZIkSVLGU88kSZLaIE8dkyRJrcEZRZIkSZIkSQKcUSRJkqQ6OKNJkqT2yRlFkiRJkiRJAmwUSZIkSZIkKWOjSJIkSZIkSYCNIkmSJEmSJGVsFEmSJEmSJAmwUSRJkiRJkqSMjSJJkiRJkiQB0Km1C1D7MXja4BrLX634KmdMO6N6edGERS1dkiRJUp38vUWS1F4VPaMoIvpHxKyIeDIinoiIM7P1F0bEioiozG6H52XOi4ilEfF0RBzWFA9AkiRJkiRJTaOUGUXvAWellBZERA9gfkTck227MqX0w/ydI2J34AvAHsAOwL0RsXNKaWMJNUiSJKkNKnVGjjN6JElqHUXPKEopvZxSWpDdXwssBnZsIDIemJFS+ldK6R/AUmBUseNLkiRJkiSpaUVKqfSDRAwAHgD2BL4JTATeBOaRm3X0RkRcA8xJKd2UZa4D7kop3VrrWJOASQB9+/YdMWPGjHrHXbduHRUVFUXXbb5l80+uerLG8rYdt+W1ja9VL+/eZ3fzZZIv59rNmzdfPvlyrt28+drK7fe2zSlfzrWbN2++fPNtvfYxY8bMTymNrGtbyY2iiKgA/g+4JKX0h4joC6wEEvB9YPuU0omFNoryjRw5Ms2bN6/esWfPns3o0aOLrt18y+brmkJ+7bprq5eLmYJuvnXy5Vy7efPmyydfzrWbN19buf3etjnly7l28+bNl2++rdceEfU2ioo+9Sw7cGfg98D0lNIfAFJKr6SUNqaU3gd+yb9PL1sB9M+L98vWSZIkSZIkqQ0o5VPPArgOWJxSuiJv/fZ5ux0NPJ7dnwl8ISK6RMRAYBAwt9jxJUmSJEmS1LRK+dSz/YEvA4siojJbdz7wxYgYSu7Us2XAyQAppSci4hbgSXKfmHaan3gmSZKkzZGf2iZJKldFN4pSSg8BUcemOxvIXAJcUuyYkiRJkiRJaj4lXaNIkiRJkiRJmw8bRZIkSZIkSQJsFEmSJEmSJClTysWsJUmSJDWDUi+G7cW0JUnFckaRJEmSJEmSABtFkiRJkiRJynjqmSRJkqQm5alvklS+bBSpYP7AlyRJah/8vU+S2i8bRZIkSZLaFBtVktR6vEaRJEmSJEmSABtFkiRJkiRJynjqmSRJkqTNSv6pa562JkmN44wiSZIkSZIkAc4oale8KKAkSZLUMH9nltTe2SiSJEmSpDbCRpWk1majSJIkSZKaiI0eSeXORlEZ8YeOJEmSJElqTjaKJEmSJEmAf5yW1AqNoogYB/wE6Aj8KqU0paVraC2+6UqSJElqTq39f47WHl9S6Vq0URQRHYGfAocAy4FHImJmSunJlqxDkiRJktT2lNposlElla6lZxSNApamlJ4DiIgZwHigLBpFvulIkiRJ0uartf/PZ6NMbUGklFpusIhjgXEppa9ky18G9kkpnZ63zyRgUra4C/B0A4fcBlhZQknmzZsvv7HNmzfffvPlXLt58+Z97zBv3nz7yrf12j+cUtq2zi0ppRa7AceSuy5R1fKXgWtKON68Eusxb958mY1t3rz59psv59rNmzfve4d58+bbV76ca+9Ay1oB9M9b7petkyRJkiRJUitr6UbRI8CgiBgYEVsAXwBmtnANkiRJkiRJqkOLXsw6pfReRJwO/AXoCExNKT1RwiF/UWJJ5s2bL7+xzZs3337z5Vy7efPmWy9fzrWbN2++fPNlW3uLXsxakiRJkiRJbVdLn3omSZIkSZKkNspGkSRJkiRJkgAbRZIkSZIkScrYKJIkSc0qIn4QEV9v7TpaS0RcHxEXt3YdpYqIvhGxOCK6tHYtkiSp+dgokiRJzSYitgWOB37eijWMjojlrTX+5iKl9AowC5jU2rVIkqTmY6NIkiQ1p4nAnSml9a1diJrEdODk1i5CkiQ1HxtFkiSpOX0S+L/8FRExPiIqI+LNiHg2IsZl63eIiJkR8XpELI2I/8rL1Dh9q/YsoYhYFhHfioiFEbEmIm6OiK4RsSVwF7BDRKzLbjtsqujseGdnx3srIq7LTr26KyLWRsS9EbF13v6/i4h/ZmM/EBF7NHDsI7LHvzoi/hYRQwp5IiNim4i4I8u9HhEPRkSHbFv/iPhDRLwWEasi4pps/Ucj4v5s3cqImB4RvYp9nMDDwEci4sOF1CxJksqPjSJJktScBgNPVy1ExCjgBuBsoBfwCWBZtnkGsBzYATgWuDQiDmrEWJ8DxgEDgSHAxJTSW+SaVS+llCqy20sFHu8zwCHAzsCnyTWczge2Jfc71Nfy9r0LGARsBywgN/PmAyJiGDCV3KycPuROyZtZ4HV/ziL3/GwL9M1qSRHREbgDeB4YAOxI7rkECOAH5J7T3YD+wIXFPs6U0nvAUmCvAuqVJEllyEaRJElqTr2AtXnLJwFTU0r3pJTeTymtSCk9FRH9gf2Bc1NKG1JKlcCvyF3fqFBXpZReSim9DvwvMLTE2q9OKb2SUloBPAg8nFJ6NKW0AbgNGFa1Y0ppakppbUrpX+QaMXtFRM86jjkJ+HlK6eGU0saU0jTgX8C+BdTzLrA98OGU0rsppQdTSgkYRa4RdHZK6a3s+Xsoq2tp9lz/K6X0GnAFcGCxjzOzltz3VZIkbYZsFEmSpOb0BtAjb7k/8Gwd++0AvJ5Sym8qPU9udkyh/pl3/22gohHZurySd399HcsVABHRMSKmZKfRvcm/Z0htU8cxPwyclZ0+tjoiVpN7TjZ5OhxwObnZPHdHxHMRMTlb3x94PpvtU0N2GtmMiFiR1XZTHXUV9Djz9ABWF1CvJEkqQzaKJElSc1pI7pSmKi8CH61jv5eA3hGR31TaCViR3X8L6J637UONqCE1Yt9i/CcwHjgY6Enu9C/InfZV24vAJSmlXnm37iml325qkGzG0lkppY8ARwLfjIix2TF3iohOdcQuJff4B6eUtgK+VE9dBcnG+BjwWLHHkCRJbZuNIkmS1JzupOapTtcBJ0TE2IjoEBE7RsSuKaUXgb8BP8guQj2E3GlqN2W5SuDwiOgdER8Cvt6IGl4B+uSfCpZdDLupGkg9yJ0+topcM+vSBvb9JXBKROwTOVtGxKeqGmTZRbuvryuYXQT7YxERwBpgI/A+MBd4GZiSHa9rROyfV9s6YE1E7Eju2lClGAUsSyk9X+JxJElSG2WjSJIkNacbyDV4ugGklOYCJwBXkmt2/B+507EAvkhuNs5L5K6Nc0FK6d5s243kZrEsA+4Gbi60gJTSU8Bvgeey0712IHe61t9KeWB5biB3mtwK4ElgTgO1zAP+C7iG3Gl5S4GJebv0B/5aT3wQcC+5xs/fgf9JKc1KKW0kdxHqjwEvkLvg9eezzEXAcHLP9Z+APzT60dV0HPCzEo8hSZLasMhdA1GSJKl5RMSlwKsppR+3di1VIuJXwO9SSn9p7VqqRMQW5JphQ1JK77Z2PbVFxHbkGnvDsgtdS5KkzZCNIkmSJEmSJAGeeiZJkiRJkqSMjSJJkiRJkiQBNookSZIkSZKU6dTaBTRkm222SQMGDKh3+1tvvcWWW25Z9PHNmzdfXL6cazdv3nz55su5dvPmzfveYd68+faVb+u1z58/f2VKads6N6aU2uxtxIgRqSGzZs1qcPummDdvvvzGNm/efPvNl3Pt5s2bb718Oddu3rz58s239dqBeameXoynnkmSJEmSJAko4RpFEdE1IuZGxGMR8UREXJStHxgRD0fE0oi4OSK2yNZ3yZaXZtsHNNFjkCRJkiRJUhMoZUbRv4CDUkp7AUOBcRGxL3AZcGVK6WPAG8BJ2f4nAW9k66/M9pMkSZIkSVIbUfTFrLNz2tZli52zWwIOAv4zWz8NuBC4Fhif3Qe4FbgmIiI7jiRJkiRJKtK7777L8uXL2bBhQ/W6nj17snjx4qKPab74fFupvWvXrvTr14/OnTsXnI1S+jQR0RGYD3wM+ClwOTAnmzVERPQH7kop7RkRjwPjUkrLs23PAvuklFbWOuYkYBJA3759R8yYMaPe8detW0dFRUXR9Zs3b764fDnXbt68+fLJb3jiiRrL72y3HVu8+mr1ctc99mi2sc2bN7/55Mu5dvPmG5OvqKigb9++9OzZk4gAYOPGjXTs2LHo8c0Xn28LtXfo0IE1a9bwyiuvsG7duhrbx4wZMz+lNLLOcH1XuW7MDegFzAI+DizNW98feDy7/zjQL2/bs8A2DR3XTz0zb75t5su5dvPmzZdP/slddq1xu+uaa2osN+fY5s2b33zy5Vy7efON+rn55JPp/fffr7HuzTffLGl888Xn20rt77//fnryySc/sJ3m/tSzlNLqrFG0H9ArIqpOaesHrMjur8gaR2TbewKrmmJ8SZIkSZLau6qZRFKVYl4TRV+jKCK2Bd5NKa2OiG7AIeQuUD0LOBaYAUwAbs8iM7Plv2fb78+6WJIkSU1u8a671VjecMbpLD7lq9XLuz1V/Hn/kiRJm6uiG0XA9sC07DpFHYBbUkp3RMSTwIyIuBh4FLgu2/864MaIWAq8DnyhhLElSZIkSVI9av/BpFSF/IFl/fr1jBs3jvvvv7+k6+sU6tJLL+X8889v9nEqKio+cI2ftmbRokX86Ec/4vrrry/5WKV86tlCYFgd658DRtWxfgPw2WLHkyRJkiRJbdfUqVM55phjWqRJBC3XKCoHgwcPZvny5bzwwgvstNNOJR2rSa5RJEmSJEmS2rfp06czfvz46uUrr7ySwYMHs9deezF58mQAKisr2XfffRkyZAhHH300b7zxBgCjR49m3rx5AKxcuZIBAwYAcP3113PMMccwbtw4Bg0axDnnnAPA5MmTWb9+PUOHDuW4445rsK4BAwZw3nnnMXToUEaOHMmCBQs47LDD+OhHP8rPfvYzIPcJc2PHjmX48OEMHjyY22+/vc5jXX755ey9994MGTKECy64YJPPyeTJk9l9990ZMmQI3/rWtwB45ZVXOProo9lrr73Ya6+9+Nvf/gbAUUcdxYgRI9hjjz349a9/XX2MiooKzj77bPbYYw8OPvhg5s6dy+jRo/nIRz7CzJkzq/f79Kc/TUOfHF8oG0WSJEmSJKkk77zzDs8991x1g+euu+7iT3/6Ew8//DCPPfZYdYPn+OOP57LLLmPhwoUMHjyYiy66aJPHrqys5Oabb2bRokXcfPPNvPjii0yZMoVu3bpRWVnJ9OnTN3mMnXbaicrKSg444AAmTpzIrbfeypw5c6qbPV27duW2225jwYIFzJo1i7POOoval1W+++67WbJkCXPnzqWyspL58+fzwAMP1DvmqlWruO2223jiiSdYuHAh3/72twH42te+xoEHHshjjz3GggUL2GOPPYDcjKz58+czb948fvazn7FqVe7zv9566y0OOuggnnjiCXr06MG3v/1t7rnnHm677Ta++93vVo83cuRIHnzwwU0+F5tSyjWKJEmSJEmSWLlyJb169apevvfee/nSl75E9+7dAejduzdr1qxh9erVHHjggQBMmDCBz35201eoGTt2LD179gRg99135/nnn6d///6Nqu/II48EcqdorVu3jh49etCjRw+6dOnC6tWr2XLLLTn//PN54IEH6NChAytWrODVV19lq622qj7G3Xffzd13382wYbmr8Kxbt44lS5bwiU98os4xe/bsSdeuXTnppJM44ogjOOKIIwC4//77ueGGGwDo2LFj9WO76qqruO222wBYsWIFS5YsoU+fPmyxxRaMGzeuuv4uXbrQuXNnBg8ezLJly6rH22677XjppZca9bzUxUaRJEmSJEkqSbdu3diwYUPR+U6dOvH+++8DfOA4Xbp0qb7fsWNH3nvvvUYfv+oYHTp0qHG8Dh068N577zF9+nRee+015s+fT+fOnRkwYMAH6kgpcd5553HyyScX/Jjmzp3Lfffdx6233so111zD/fffX+e+s2fP5t577+Xvf/873bt354ADDqgev3PnztUfc59ff1XtVTZs2EC3bt0KfEbq56lnkiRJkiSpJFtvvTUbN26sbm4ccsgh3HTTTbz99tsAvP766/Ts2ZOtt966+vSoG2+8sXp20YABA5g/fz4At956a0Fjdu7cmXfffbd6eezYsaxYsaKo+tesWcN2221H586dmTVrFs8///wH9jnssMOYOnVq9SegVc06qm/sdevWsWbNGg4//HCuvPJKHnvssep9r732WgA2btzImjVrWLNmDVtvvTXdu3fnqaee4pFHHmn0Y3jmmWfYc889G52rzRlFkiRJkiRtZnZ7ajFr166lR48eRR+jsflDDz2Uhx56iIMPPphx48bx8MMPM3LkSLbYYgsOP/xwLr30UqZNm8Ypp5zC22+/zUc+8pHqizZ/61vf4nOf+xy/+MUv+NSnPlXQeJMmTWLIkCEMHz6cG2+8kaVLl9K7d++iHutxxx3Hpz/9aQYPHszIkSPZdddd63x8ixcvZr/99gNyF5m+6aab2Gabbeoce+3atYwfP54NGzaQUuKKK64A4Cc/+QmTJk3iuuuuo2PHjlx77bWMGzeOn/3sZ+y2227ssssu7L333o1+DLNmzSr4uWuIjSJJkiRJklSy0047jSuvvJKDDz4YgG9+85sf+GSwoUOHMmfOnA9kd911VxYuXFi9fPHFF7N27VomTpzIxIkTq9ffcccd1fcvu+wyLrvsMgAef/xxPvOZz9R56lX+dXxqHy9/29///vcaubVr1wJUzyACOPPMMznzzDNr7Fff2Ntvvz1z5879QD19+/at81PV7rrrrhpjVzXp8se/8MILa2Sqtv3rX/9i3rx5/PjHP/7AcRvLU88kSZIkSVLJhg8fzpgxY9i4cWOLj73nnntWz9hpT2NXeeGFF5gyZQqdOpU+H8gZRZIkSZIkqUmceOKJrV1CuzRo0CAGDRrUJMdyRpEkSZIkSZuBlFJrl6A2ppjXhI0iSZIkSZLKXNeuXVm1apXNIlVLKbFq1Sq6du3aqJynnkmSJNVh8a671VjecMbpLD7lq9XLuz21uKVLkiSpXv369WP58uW89tpr1es2bNjQ6CZBPvPF59tK7V27dqVfv36NytookiRJkiSpzHXu3JmBAwfWWDd79myGDRtW9DHNF58v59o99UySJEmSJEmAM4okSVIz8vQtSZKk8uKMIkmSJEmSJAE2iiRJkiRJkpSxUSRJkiRJkiTARpEkSZIkSZIyNookSZIkSZIE2CiSJEmSJElSxkaRJEmSJEmSABtFkiRJkiRJytgokiRJkiRJEmCjSJIkSZIkSZlOrV2AJEmS2p7Fu+5WY3nDGaez+JSvVi/v9tTili5JkiS1ABtFkiSpXjYLJEmS2hdPPZMkSZIkSRJQQqMoIvpHxKyIeDIinoiIM7P1vSPinohYkn3dOlsfEXFVRCyNiIURMbypHoQkSZIkSZJKV8qpZ+8BZ6WUFkRED2B+RNwDTATuSylNiYjJwGTgXOCTwKDstg9wbfZVkiRJTczTBiVJUjGKnlGUUno5pbQgu78WWAzsCIwHpmW7TQOOyu6PB25IOXOAXhGxfbHjS5IkSZIkqWlFSqn0g0QMAB4A9gReSCn1ytYH8EZKqVdE3AFMSSk9lG27Dzg3pTSv1rEmAZMA+vbtO2LGjBn1jrtu3ToqKiqKrtu8efPF5cu5dvPmzTcuv+GJJ2osv7Pddmzx6qvVy1332KPZ8q05dlvI11ZO37u6lNtr3/zmky/n2s2bN1+++bZe+5gxY+anlEbWuTGlVNINqADmA8dky6trbX8j+3oH8PG89fcBIxs69ogRI1JDZs2a1eD2TTFv3nz5jW3efLnln9xl1xq3u665psZyc49far7U+kvJt+bYbSFfWzl975qifvPmmypfzrWbN2++fPNtvXZgXqqnF1PKNYqIiM7A74HpKaU/ZKtfiYjtU0ovZ6eWVf3paQXQPy/eL1snSZKkWrzGkCRJag2lfOpZANcBi1NKV+RtmglMyO5PAG7PW3989uln+wJrUkovFzu+JEmSJEmSmlYpM4r2B74MLIqIymzd+cAU4JaIOAl4Hvhctu1O4HBgKfA2cEIJY0uSJEmSJKmJFd0oSrmLUkc9m8fWsX8CTit2PEmSJEmSJDWvkq5RJEmSmpfXqZEkSVJLKvoaRZIkSZIkSdq82CiSJEmSJEkSYKNIkiRJkiRJGRtFkiRJkiRJAmwUSZIkSZIkKWOjSJIkSZIkSYCNIkmSJEmSJGVsFEmSJEmSJAmwUSRJkiRJkqSMjSJJkiRJkiQBNookSZIkSZKU6dTaBUiSpOazeNfdaixvOON0Fp/y1erl3Z5a3NIlSZIkqQ1zRpEkSZIkSZIAG0WSJEmSJEnK2CiSJEmSJEkSYKNIkiRJkiRJGRtFkiRJkiRJAmwUSZIkSZIkKWOjSJIkSZIkSQB0au0CJEmSpNoW77pbjeUNZ5zO4lO+Wr2821OLW7okSZLaBWcUSZIkSZIkCXBGkSRJkpqBM4IkSSpPziiSJEmSJEkS4IwiSZIa5KwISZIktSfOKJIkSZIkSRJgo0iSJEmSJEkZG0WSJEmSJEkCSmwURcTUiHg1Ih7PW9c7Iu6JiCXZ162z9RERV0XE0ohYGBHDSy1ekiRJkiRJTafUGUXXA+NqrZsM3JdSGgTcly0DfBIYlN0mAdeWOLYkqQws3nW3GrcNTzxRY1mSJElS21FSoyil9ADweq3V44Fp2f1pwFF5629IOXOAXhGxfSnjS5IkSZIkqel0aoZj9k0pvZzd/yfQN7u/I/Bi3n7Ls3UvI0lSPfx4ekmSJKnlREqptANEDADuSCntmS2vTin1ytv+Rkpp64i4A5iSUnooW38fcG5KaV6t400id2oaffv2HTFjxox6x163bh0VFRVF127evPni8uVcu/mWz2944okay+9stx1bvPpq9XLXPfZo1nxt5VZ/e86Xc+3mWz9fW7m9d5pvunw5127evPnyzbf12seMGTM/pTSyrm3N0Sh6GhidUno5O7Vsdkppl4j4eXb/t7X3q+/YI0eOTPPmzatvM7Nnz2b06NFF127evPni8uVcu/mWz9eeEfT8Gafz4auvqV7e1IygUvO1lVv97TlfzrWbb/18beX23mm+6fLlXLt58+bLN9/Wa4+IehtFpV7Mui4zgQnZ/QnA7Xnrj88+/WxfYE1DTSJJkiRJkiS1rJKuURQRvwVGA9tExHLgAmAKcEtEnAQ8D3wu2/1O4HBgKfA2cEIpY0uSWobXCJIkSZLaj5IaRSmlL9azaWwd+ybgtFLGkySpsWx0SSqG7x2SpPaqOT71TJIkSWpVNnokSSpOc1yjSJIkSZIkSWXIRpEkSZIkSZIAG0WSJEmSJEnK2CiSJEmSJEkSYKNIkiRJkiRJGT/1TJIkSWpifuqaJKlcOaNIkiRJkiRJgI0iSZIkSZIkZWwUSZIkSZIkCbBRJEmSJEmSpIyNIkmSJEmSJAE2iiRJkiRJkpTp1NoFSFJb50ccS5IkSWovnFEkSZIkSZIkwBlFktTsWntGUmuPL0mSJKl8OKNIkiRJkiRJgI0iSZIkSZIkZTz1TJIkSWpjPG1YktRanFEkSZIkSZIkwEaRJEmSJEmSMp56JqnNc/q9JEmSJLUMG0WSNnvl3mgq9/olSZIklQ9PPZMkSZIkSRLgjCJJkiRJTczZsJJUvmwUSWWi1F+4mjLvL3uSJLVtNmokScXy1DNJkiRJkiQBziiSJEmS1MY4I0qSWo8ziiRJkiRJkgS0woyiiBgH/AToCPwqpTSlpWuQJEmSVL/2PKOnta8LKUmtrUUbRRHREfgpcAiwHHgkImamlJ5syTqkYvhDv3g+d5IkSZJUHlp6RtEoYGlK6TmAiJgBjAdsFLUDrf3XmdZuVrT2+JIkSe1Fe/601nL/nVlS64uUUssNFnEsMC6l9JVs+cvAPiml0/P2mQRMyhZ3AZ5u4JDbACtLKMm8efPlN7Z58+bbb76cazdv3rzvHebNm29f+bZe+4dTStvWuSWl1GI34Fhy1yWqWv4ycE0Jx5tXYj3mzZsvs7HNmzfffvPlXLt58+Z97zBv3nz7ypdz7S39qWcrgP55y/2ydZIkSZIkSWplLd0oegQYFBEDI2IL4AvAzBauQZIkSZIkSXVo0YtZp5Tei4jTgb8AHYGpKaUnSjjkL0osybx58+U3tnnz5ttvvpxrN2/efOvly7l28+bNl2++bGtv0YtZS5IkSZIkqe1q6VPPJEmSJEmS1EbZKJIkSZIkSRJgo0iSJEmSJEkZG0UqWkRs19o1SJLUXvhzV63F155ak68/qeXZKCpQRPSMiCkR8VREvB4RqyJicbauV2vXtykR8aGIuDYifhoRfSLiwohYFBG3RMT2BeR717r1AeZGxNYR0bsFHkJdNfVpoXFGRsSsiLgpIvpHxD0RsSYiHomIYS0wfqeIODki/hwRC7PbXRFxSkR0LvHYm7wSfkR0zMb/fkTsX2vbtwvId4+IcyLi7IjoGhETI2JmRPx3RFQUWfczjdh3SN79zhHx7Wz8SyOiewH50yNim+z+xyLigYhYHREPR8TgAvJ/iIgvlfBYPxIRUyPi4oioiIhfRsTjEfG7iBhQQL5DRJwYEX+KiMciYkFEzIiI0QVkfe198Ji+9lrgtZfl/bnbTn/uZmO19s/esn39+doreSxfeyXw9VfyWK32+gt/76vrmOX0e19Jv7fVVjaNotZ+0wZuAd4ARqeUeqeU+gBjsnW3lHLgiLirgH22iogfRMSNEfGftbb9TwHDXA88CbwIzALWA4cDDwI/KyC/Epifd5sH7AgsyO5vqv5xefd7RsR12ZvPbyKibwH5KXn/cEZGxHPAwxHxfEQcWEB+QfaP9aOb2rcO/wP8N/An4G/Az1NKPYHJ2bZNjV0REd+LiCey1+xrETEnIiYWOP6NwFDgQnLfs8OBi4C9gJsKGL/2D9z8H7yHFzD+z4EDgVXAVRFxRd62YwrIXw/0BQaSew5HApcDAVxbQP1rI+LN7LY2ItYCH61aX+D4VaYAHwN+BHSjsNf+V1NKK7P7PwGuTCn1As4tML8PcBTwQvZL0tERsUUBuSrXA48A64A5wFPAJ4E/A1MLyF8H7AT8gNy//Tuydd+OiDM2kfW152uvtV574M/d9vxzF1r/Z285v/6ux9eer726a/O9b9P59vz68/e+8v6973pK+72tppRSWdyAudkD/SK5N55js/Vjgb8XkK8Avgc8AawBXsuewIkFjv90Mdvy9hlez20E8HIB+d+Te8EdBczMlrtk2xYUkH807/4LtbZVFpA/K3uRDc5b949GfP8W5N3/FXAx8GHgG8AfC8gvyrs/C9g7u78zMK+A/D+AHwIvZK+lbwA7FFh7Q8/dowXkbwcmAv2AbwLfAQYB04BLC8g/U8y2vH02As9lz0HVrWr5nQLyC/PudwJ+AfwB6FLg46/MvgbwTyDylhcWkL8KuAHoW+RrL//7Vwl0buT4T+fdf6S+52ZT4wNbAV8G7iT3/vNr4NAWeP0trLU8J/vaBVjsa8/XXlt87dV+/I3ZlrePP3f/fb+sfu420euv1J+9Zfv687Xna6+1Xnu+/sr79Ye/920Wv/cV+9r5wPEaG2itW2v+o8nydwPn1Hrh9CXX4bu3gPxG4P7sDaf2bX0B+cpay/8P+CvQh8LetB/Lu39xrW2LNpXP9usH/A64AugBPNeI71/+m3btx1JZQH4x0Cm7P6ex9dca/wByHfl/Zs//pE1k/w4cCnwWeB44Klt/IIX9wHis1vIj2dcOwFMF5OdkY3fIW9cB+DzwcAH5JcBO9Wx7sYD8B2oELshef0sa89oFpjb03DRwjBHZv5+vZY+9Ma+958j9FeIz1PrPaSHjA5eQ69B/BDgf+Dq5XzhOAO5ozGsvb10f4BTg/gLy88n9cjKK3F/ZRmbrP0ZhPzTmAx/N7g8HHsjb9qSvvWZ/7R29Gbz29m7p1162z+b8c3eTz1+2X7v8uZtlWvtnb9m+/nzt+dprrdeer7/yfv3h731Q3v/nqO//DIMK/bdX43iNDbTWrTX/0WT7bQ1cRm4K1xvA69kbyWVA7wLyjwOD6tlWyD+cxfn/aLN1E8nNkHq+gPz3gIo61n8MuLWR34sjszeSfzYis5xcg+6s7B9R5G0r5D8cZ5D7wXkQuemQP8m+9xcBNxaQr+s/TB2BccCvN5HdC/gLcBewazb26uy5/48Cxv4b8PG85+4vedsK+cvQAOBm4FXgmez2arZuYAH504C96nteC8jfBIyrY/1XgHcLyP+qntfeR4GHGvEa6kDuTftB4KVG5H5d69Y3W/8h4L4CjzEReJjcm+5aclOqLwV6FpB9oNBa68mPBZ7O3gM+Tu4ve0uy18D4AvIHkfur1hJyf9HZJ1u/LfDfBb72Xsted1Xj+torLHd9E7z2Tmijr72jGvHaW5q99vYt9LWX7efP3X9n2tXP3WzfoXzwZ+8b2fO/fwH5Un/2lu3rz9feB/+A3MjXXl2/9/na872vNV9/q2mB/3fg/zmq9i/l/xxTab3/c5T0f4YPHK+xgda6teY/mrz9dgUOrv0CrOsFXUf2WGCXerYdVUD+v4GD61g/jgI6rHn1jy2m/tp5cuda7tmIx39Brdu22foPATcUOP7o7I3qUWARudMoJpFN69tEdkaJr7/din3ustfuXHI/7B+qeh2Q+8/S1wocfx9y3eE+wP7At4DDG1H/KP49dXZ3cj9AWzv/KfJ+eDcifwDw3UaOv08T1r8HuV8+WvL526fW+I39/u9XyvhZrk92u6kxuTqOU9C/9+bOF/raq5XdHljVyvVv8pfUZh7/Dmr9B2IT+wewTVOMn/3bP4sCTpvL9m/1n7u1ch/P/u0VVH89j//bjXj8Zf1ztwm+/7V/9u6crS/oZ2/2vtszu9+d3H9+7yD3n/Webfn1l9W+VXa/G7n/4P5vI2rPz3fParm3Efmyfu3V+t53K+J7P6TE197XgP4l1N+q731NUH+NPHn/5ygw3xSvvzGt+Por9fmrev2tbuzrD9gCmAAcQu53vuPIzYg6rcDH3gU4vur1A/wncE0T5bdogfG3qJX/Mrlm46mNqH9CE45/HPDTRuYn1MoX/P2rfas6b6+sRcQJKaVfb2KfIeS6jIPINZdOTCk9ExHbAl9MKV21ifzXyD3Ji8n9levMlNLt2bYFKaXhBdS5K7mLsT2cUlqXt35cSunPJeQ/mVJq8OJ02YVDTy+2/jb++Js1nz32U8n9ZWcoxT323bKx5zS29oi4gNz1uToB95BrOswm9yb+l5TSJY3M70Nu+mu55kt9/K2dL5vHHxEz61h9ELkpuaSUjtzE2LXzQe6Xr3LNQ2mPv7Xz5fb456aURmX3v0LuZ9Afyc0u/t+U0pSG8nUc7+PkXv+Pp5Tubky2mHyt+v+LXP23FVp/HflTad3Hf0CWX9RKz1+LPv6IeILcX8bfi9yn9bxF7q+zY7P1DV5YNSL2IXfqwZsR0Q04DxhG9tfhlNKaAvJPpZTWNDZfR+1vA7c2ovameOxVtXcndxHexj72queuKj+c3O/vjc0X89w39eM/N6u/0PHXZGM+C/wG+F369wVuNyn7vfW2lNKLhWaaOJ9f/2/J1f9aCflbyuzxt/bzV/T4ETGd3O+L3chd03dLcj+3xpL7A9uEAvPdyTWqKshdY6gp8qSUJrZwvikff0uOX9T37wMa21lqizdqXbOoiPwJBeyziGw2CblpefPINQygsGskfY3cVLA/AsvIm/5FYef7nlFivtT6S82XWn+rPX9N9L1/qsTvXUdybxpvUvOvhIVMoTVvvqg8uU8YuYncX3YPzL6+nN0/sICxHy3zfGs//tYev9Xzefcf4d9/Fd6Swq4TMTfv/n+Ru7DkBeSudTC5BfKl1t+Uj/8rJT7+r2Tfz3J6/kp9/Ivz7i+ota2ygPwT/Ps6J78AfkxuVtkFwB+aM98Etbe1x35lSz13beTxP0ru1JdDyX1S5GvkLu48Aejx/9u7nxBJzjqM499nM9G4WXaNGmKCGr0MKgQWDbISwRyEHMxFPCl4UkTFBLx78CSeFEG8SHLwsBFWSfCkewgGEpTEsNk1UQnoLsR/gxAJicG4MT8Pbw1OOts7NVM9/W6t3w8U0901T7+/t2amp3m76n1H5F8A/kK7bOYr2387Y7cV5KfWfyX1/8vsOCt2Jsdvsf7R7TO8L6QNNmwB1wz3x07GbH7G+Tc8314DvTbg3JLtN8ArE59714Em4JmF+0eGP9pvM+6fRu+Bmqn1z73/+85fAX0/c6nbw/0x7Zs3v6887Y3K12hnIh0fHtvLpH7mzU/Jn6XN1fF2FuYiXPxdXpI/s+N2j4GaqfXPvf+9j9/U9k8xfJBIm2tie1LQTRZWo1mS7zZYs4LaZ9v3FeV7938xcy1t2owHgL+PyJ+h70DN1Prn3v/ex2/f7dPmt3oT7bX3RYY5sYDrYNRqpeZnnF/cNpiPm4C7aNf77hTa/EOXleTcsl3Dc+9mK8nxqnoKoKpeSnI3bcKq20bkD9VwyVFVXUhyJ/DjJLcONRx0fmr9c+//lHzvvv87yeGqepk2Ez8ASY4Br5k3f1D5qnoN+E6SU8PXLRj/f8O8+Sl54BhtBY8AleTmqvprkiOMfN1PcgPtDXNqOHW/qv6Z5NU15KfWP/f+9z5+U9v/AvDdJF+nTSr6yyTPAc8N+3bzdP43NcLZJLdX1a+TbAIXDzg/tfY5930V+d79f93vd1VdpC1T/9O0S9l2U8Pr72ngdJJraZegf4a2bPuNB5yfWv/c+9/7+E1p/z7aVRDX0Fa7O5Xkj8AJ4Ecj2jY/7/zr7XVkqdc2dPxjS/adHJHfos0vc+vC9l5GzGZOW6bxnUv2jVkB4WGGT1R3PLYB/BD4zxryU+ufe//3nb8C+v7mJY+/A7jNvPmDzC9kPkmbX2F0xrz5VeV3PM9hxq2+coE2CeX54evNw+NHGPep/qT81Prn3v/ex29V7QNHaRNjf5gdy5WPyB2jrXz4B9oKNheHOh5hyapAq8xPqX3ufV/Fsevc/829/qwW8mcus+/wGvJT6597/3sfv6nt3wLcMtx+K21y9I/soX3zM87v3K6KyazHSHIfbUnCRy+x72RVffaA238X8GpV/e0S++6oqscOMt9b7/73PH5z/9lJ0tVk+ET2pqo63yPfW+/+9z5+624/yVHgfbQPiP5UVVvrzPfUu++9j12v9pNsVtWzvfK99e5/7+PXu31dPf5vBookSZIkSZJ0eYd6FyBJkiRJkqQrgwNFkiRJkiRJAhwokiRJkiRJ0sCBIkmSJEmSJAEOFEmSJI2W5KEkTyZ5JskXh8c+n+TZJI8n+UGS7w2P35jkJ0meGLY7+lYvSZK0O1c9kyRJGinJ26rq+SRvAZ4A7gIeAz4EvAg8DJytqq8mOQl8v6oeTfIe4OdV9YFuxUuSJI2w0bsASZKkGbk3yaeG2+8GPgc8UlXPAyQ5BWwO+z8BfDDJdvZokiNV9dI6C5YkSdoLB4okSZJGSHInbfDno1X1cpJfAL8Hlp0ldAg4UVX/WkuBkiRJK+AcRZIkSeMcA/4xDBK9HzgBXA98PMkNSTaAT+/4/tPAPdt3khxfZ7GSJEn74UCRJEnSOD8DNpL8DvgW8Cvgz8A3gcdpcxVdAF4Yvv9e4PYk55L8FvjS2iuWJEnaIyezliRJmmB73qHhjKIHgfur6sHedUmSJO2HZxRJkiRN840kTwFPA+eBh7pWI0mSNIFnFEmSJEmSJAnwjCJJkiRJkiQNHCiSJEmSJEkS4ECRJEmSJEmSBg4USZIkSZIkCXCgSJIkSZIkSYP/ArQqcF7uPmnKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_age_pdf.set_index([\"age\", \"gender\", \"scam\"]).sort_index().unstack(level=[\"gender\", \"scam\"]).plot(figsize=(20,10), subplots=True, kind='bar', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicity_pdf = profiles_df.withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").groupBy(F.col(\"ethnicity\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='ethnicity'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ethnicity_pdf.set_index([\"ethnicity\", \"scam\"]).sort_index().unstack(level=1).plot(figsize=(20,10), subplots=False, kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "children_pdf = profiles_df.withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").groupBy(F.col(\"children\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='children'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "children_pdf.set_index([\"children\", \"scam\"]).sort_index().unstack(level=1).plot(figsize=(20,10), subplots=False, kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> No data for scammers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marital status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------------------------+\n",
      "|status                             |\n",
      "+-----------------------------------+\n",
      "|null                               |\n",
      "|civil engineer                     |\n",
      "|divorce                            |\n",
      "|divorced                           |\n",
      "|i am taken                         |\n",
      "|in relationship                    |\n",
      "|married                            |\n",
      "|married (and looking for marriage) |\n",
      "|separated                          |\n",
      "|seperated                          |\n",
      "|seprated                           |\n",
      "|signle                             |\n",
      "|single                             |\n",
      "|singles                            |\n",
      "|singlescammer’s real name: olusegun|\n",
      "|widow                              |\n",
      "|widowed                            |\n",
      "|widower                            |\n",
      "|windoew                            |\n",
      "|window                             |\n",
      "|window – his spelling              |\n",
      "+-----------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.select(F.col(\"status\")).distinct().sort(\"status\").show(30, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "status_pdf = profiles_df.withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").withColumn(\n",
    "    \"status\",\n",
    "    F.when(\n",
    "        F.col(\"status\").startswith(\"wi\"),\n",
    "        F.lit(\"widowed\"),\n",
    "    ).when(\n",
    "        F.col(\"status\").startswith(\"di\"),\n",
    "        F.lit(\"divorced\"),\n",
    "    ).when(\n",
    "        F.col(\"status\").startswith(\"sep\"),\n",
    "        F.lit(\"separated\"),\n",
    "    ).when(\n",
    "        F.col(\"status\").startswith(\"married\"),\n",
    "        F.lit(\"married\"),\n",
    "    ).when(\n",
    "        F.col(\"status\") == \"in relationship\",\n",
    "        F.col(\"status\"),\n",
    "    ).when(\n",
    "        F.col(\"status\").startswith(\"sing\"),\n",
    "        F.lit(\"single\"),\n",
    "    ).otherwise(F.lit(\"other\")),\n",
    ").groupBy(F.col(\"status\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='status'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "status_pdf.set_index([\"status\", \"scam\"]).sort_index().unstack(level=1).plot(figsize=(20,10), subplots=False, kind=\"barh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Not significant source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Religion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles_df.select(F.col(\"religion\")).distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+---------+-----+\n",
      "|scam| religion|count|\n",
      "+----+---------+-----+\n",
      "|   0|     null|  447|\n",
      "|   0|Christian|18451|\n",
      "|   0|Spiritual| 3311|\n",
      "|   0|  Atheist| 2059|\n",
      "|   1|     null| 7987|\n",
      "|   0|   Muslim|  840|\n",
      "|   0|   Jewish|  229|\n",
      "|   0|    Other|10501|\n",
      "|   0|    Hindu|  211|\n",
      "|   0| Buddhist|  227|\n",
      "+----+---------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.groupBy(\"scam\", \"religion\").count().show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> No data for scammer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8346"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles_df.select(F.col(\"occupation\")).distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------------+\n",
      "|occupation   |freq                 |\n",
      "+-------------+---------------------+\n",
      "|null         |0.3311569482411947   |\n",
      "|retired      |0.011725368818200303 |\n",
      "|estudiante   |0.01120574746402187  |\n",
      "|comerciante  |0.008991708650565935 |\n",
      "|independiente|0.008472087296387503 |\n",
      "|ingeniero    |0.007952465942209068 |\n",
      "|self employed|0.0049251067482999344|\n",
      "|abogado      |0.0048121455843481015|\n",
      "|docente      |0.004631407722025168 |\n",
      "|teacher      |0.004608815489234801 |\n",
      "|empresario   |0.004608815489234801 |\n",
      "|student      |0.004382893161331135 |\n",
      "|manager      |0.003931048505523801 |\n",
      "|chef         |0.0037954951087816008|\n",
      "|engineer     |0.003727718410410501 |\n",
      "|administrador|0.003501796082506834 |\n",
      "|enfermera    |0.002688475702053634 |\n",
      "|construction |0.002552922305311434 |\n",
      "|ama de casa  |0.002530330072521067 |\n",
      "|profesor     |0.002530330072521067 |\n",
      "+-------------+---------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.filter(F.col(\"scam\") == 0).groupBy(F.col(\"occupation\")).agg(\n",
    "    (F.count('*') / profiles_df.count()).alias(\"freq\")\n",
    ").sort(\"freq\", ascending=False).show(20, truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------+---------------------+\n",
      "|occupation           |freq                 |\n",
      "+---------------------+---------------------+\n",
      "|null                 |0.034837222962745405 |\n",
      "|military             |0.015498271694191537 |\n",
      "|self employed        |0.01134130086076407  |\n",
      "|engineer             |0.01120574746402187  |\n",
      "|student              |0.010505388247520503 |\n",
      "|self-employed        |0.004653999954815535 |\n",
      "|nurse                |0.004382893161331135 |\n",
      "|business             |0.0038632718071527007|\n",
      "|sales                |0.002598106770892167 |\n",
      "|contractor           |0.0024399611413596007|\n",
      "|construction         |0.002349592210198134 |\n",
      "|engineering          |0.0021688543478752004|\n",
      "|doctor               |0.0018977475543908004|\n",
      "|civil engineer       |0.0018299708560197004|\n",
      "|army                 |0.0013555339674220003|\n",
      "|teacher              |0.001107019406727967 |\n",
      "|model                |0.0010618349411472335|\n",
      "|manager              |9.036893116146669E-4 |\n",
      "|construction engineer|8.359126132435669E-4 |\n",
      "|us army              |8.133203804532002E-4 |\n",
      "+---------------------+---------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiles_df.filter(F.col(\"scam\") == 1).groupBy(F.col(\"occupation\")).agg(\n",
    "    (F.count('*').alias('count') / profiles_df.count()).alias(\"freq\")\n",
    ").sort(\"freq\", ascending=False).show(20, truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Not significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupation_pdf = profiles_df.withColumn(\n",
    "    \"scam\",\n",
    "    F.when(F.col(\"scam\") == 0, F.lit(\"Real\")).otherwise(F.lit(\"scam\")),\n",
    ").groupBy(F.col(\"occupation\"), F.col(\"scam\")).count().toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  }
 ],
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