AlphabetSoupCharity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliverable 1: Preprocessing the Data for a Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIN</th>\n",
       "      <th>NAME</th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10520599</td>\n",
       "      <td>BLUE KNIGHTS MOTORCYCLE CLUB</td>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10531628</td>\n",
       "      <td>AMERICAN CHESAPEAKE CLUB CHARITABLE TR</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10547893</td>\n",
       "      <td>ST CLOUD PROFESSIONAL FIREFIGHTERS</td>\n",
       "      <td>T5</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C3000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10553066</td>\n",
       "      <td>SOUTHSIDE ATHLETIC ASSOCIATION</td>\n",
       "      <td>T3</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>10000-24999</td>\n",
       "      <td>N</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10556103</td>\n",
       "      <td>GENETIC RESEARCH INSTITUTE OF THE DESERT</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EIN                                      NAME APPLICATION_TYPE  \\\n",
       "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
       "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
       "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
       "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
       "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
       "\n",
       "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
       "0       Independent          C1000    ProductDev   Association       1   \n",
       "1       Independent          C2000  Preservation  Co-operative       1   \n",
       "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
       "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
       "4       Independent          C1000     Heathcare         Trust       1   \n",
       "\n",
       "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
       "0              0                      N     5000              1  \n",
       "1         1-9999                      N   108590              1  \n",
       "2              0                      N     5000              0  \n",
       "3    10000-24999                      N     6692              1  \n",
       "4  100000-499999                      N   142590              1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "#  Import and read the charity_data.csv.\n",
    "import pandas as pd \n",
    "application_df = pd.read_csv(\"charity_data.csv\")\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T5</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C3000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T3</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>10000-24999</td>\n",
       "      <td>N</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  APPLICATION_TYPE       AFFILIATION CLASSIFICATION      USE_CASE  \\\n",
       "0              T10       Independent          C1000    ProductDev   \n",
       "1               T3       Independent          C2000  Preservation   \n",
       "2               T5  CompanySponsored          C3000    ProductDev   \n",
       "3               T3  CompanySponsored          C2000  Preservation   \n",
       "4               T3       Independent          C1000     Heathcare   \n",
       "\n",
       "   ORGANIZATION  STATUS     INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  \\\n",
       "0   Association       1              0                      N     5000   \n",
       "1  Co-operative       1         1-9999                      N   108590   \n",
       "2   Association       1              0                      N     5000   \n",
       "3         Trust       1    10000-24999                      N     6692   \n",
       "4         Trust       1  100000-499999                      N   142590   \n",
       "\n",
       "   IS_SUCCESSFUL  \n",
       "0              1  \n",
       "1              1  \n",
       "2              0  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
    "application_df = application_df.drop([\"EIN\", \"NAME\"], axis=1)\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE            17\n",
       "AFFILIATION                  6\n",
       "CLASSIFICATION              71\n",
       "USE_CASE                     5\n",
       "ORGANIZATION                 4\n",
       "STATUS                       2\n",
       "INCOME_AMT                   9\n",
       "SPECIAL_CONSIDERATIONS       2\n",
       "ASK_AMT                   8747\n",
       "IS_SUCCESSFUL                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of unique values in each column.\n",
    "application_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3     27037\n",
       "T4      1542\n",
       "T6      1216\n",
       "T5      1173\n",
       "T19     1065\n",
       "T8       737\n",
       "T7       725\n",
       "T10      528\n",
       "T9       156\n",
       "T13       66\n",
       "T12       27\n",
       "T2        16\n",
       "T25        3\n",
       "T14        3\n",
       "T29        2\n",
       "T15        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at APPLICATION_TYPE value counts for binning\n",
    "application_type_counts = application_df['APPLICATION_TYPE'].value_counts()\n",
    "application_type_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of APPLICATION_TYPE\n",
    "application_type_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3       27037\n",
       "T4        1542\n",
       "T6        1216\n",
       "T5        1173\n",
       "T19       1065\n",
       "T8         737\n",
       "T7         725\n",
       "T10        528\n",
       "Other      276\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ...?\n",
    "replace_application = list(application_type_counts[application_type_counts < 200].index)\n",
    "\n",
    "# Replace in dataframe\n",
    "for app in replace_application:\n",
    "    application_df.APPLICATION_TYPE = application_df.APPLICATION_TYPE.replace(app,\"Other\")\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "application_df.APPLICATION_TYPE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "         ...  \n",
       "C4120        1\n",
       "C8210        1\n",
       "C2561        1\n",
       "C4500        1\n",
       "C2150        1\n",
       "Name: CLASSIFICATION, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at CLASSIFICATION value counts for binning\n",
    "classification_type_counts = application_df['CLASSIFICATION'].value_counts()\n",
    "classification_type_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IlkUANTc5qkVE5MkYtIhk1rYY3rE1WmqlD3R+1os/OH1IROTZGLSIZCaNaIU6GLQAINR25SGDFhGRJ2PQIpJZd26/I5HK8spDIiLPxqBFJKNGkxm1TS0AHJ86BG698pB7aREReTIGLSIZSWusVD4KBPk6vttKSOueW5w6JCLybAxaRDKqbr1qcKC/pst7fN4ujFOHRERegUGLSEY1N00AgGB/dbdex01LiYi8A4MWkYykEa1gf8cXwgO83yERkbdg0CKSUXXriJaumyNavN8hEZF3YNAiklFNvTSixalDIqK+iEGLSEbVtjVa3Zs6lDYsvXGzGWYLb1dKROSpGLSIZFTT0HbVYXdI5YUAahtNTu8XERE5B4MWkYx6etWhRuWDAI0SQNuoGBEReR4GLSIZ3bqPVndJr6m5yXVaRESeikGLSEY9HdECgIGtr6nhiBYRkcdi0CKSkW0frYDuj2hJC+irOaJFROSxGLSIZGK2CBgarKNRAzmiRUTUJzFoEcnE2GCCaN2ZYaBfT9ZoSUGLI1pERJ6KQYtIJtKU3wCtChpV938V26YOOaJFROSpGLSIZCIFpJ5MG1pf13rVYQODFhGRp2LQIpJJTQ9vKC0Z6MepQyIiT8egRSST3o5oBQdwMTwRkadj0CKSSa9HtLi9AxGRx2PQIpKJbQ+tnq7R8uOIFhGRp2PQIpJJ29Rhz0a0pJGwuqYWmMwWp/WLiIich0GLSCY1vRzRCvJTQ6GQ6uKoFhGRJ2LQIpJJdX3rfQ57cPsdAFD6KBDkyysPiYg8GYMWkUykNVo9nToE2kbDuJcWEZFnYtAikol0n8OeTh0CgE668rCeI1pERJ6IQYtIJlLQ0vn1PGhxRIuIyLMxaBHJwGS24GazGUBvg1brbXi4RouIyCPJHrS2bt2KmJgY+Pr6IiEhAceOHeuy/NGjR5GQkABfX1+MGDEC27dvb1cmKysLY8aMgVarxZgxY7B///5ut/v8888jNjYWAQEBCA4OxiOPPIITJ07YlWlqasJTTz2FsLAwBAQEYM6cObhy5UoPzgL1N8ZbRqACfXsxddga0nhjaSIizyRr0Nq3bx+WL1+OZ555BgUFBUhKSsLMmTNRUlLSYfmioiLMmjULSUlJKCgowLp167B06VJkZWXZyuTm5iI1NRVpaWkoLCxEWloa5s+fbxeSHGn37rvvxubNm3Hq1CkcP34cw4cPR3JyMq5fv24rs3z5cuzfvx979+7F8ePHUVdXh9mzZ8NsNrvgbFFfIk0bBmpVUPooelxP24gWgxYRkUcSMpo4caJIT0+3OxYbGyvWrFnTYfmnn35axMbG2h1bvHixmDx5su3n+fPnixkzZtiVSUlJEQsWLOhxu0IIYTAYBABx6NAhIYQQNTU1Qq1Wi71799rKlJaWCh8fH/Hxxx93Wk9n9RoMBodfQ96voKRaRK/+UEzd8Emv6vlLTpGIXv2hSP/rSSf1jIiIHOHo97dsI1rNzc3Iy8tDcnKy3fHk5GTk5OR0+Jrc3Nx25VNSUnDy5EmYTKYuy0h19qTd5uZm7NixAzqdDuPHjwcA5OXlwWQy2dUTGRmJuLi4TusBrNONRqPR7kH9jzSiFdSL9VnArVOHXKNFROSJZAtalZWVMJvNCA8PtzseHh6O8vLyDl9TXl7eYfmWlhZUVlZ2WUaqszvtfvjhhxgwYAB8fX3x6quvIjs7G2FhYbZ2NBoNgoODHe4/AGzYsAE6nc72iIqK6rQs9V1tVxyqelUPpw6JiDyb7IvhFQr79SlCiHbH7lT+9uOO1OlImQcffBBfffUVcnJyMGPGDMyfPx8VFRVdvp879X/t2rUwGAy2x+XLl7usj/omaTF8UC8WwgNtI1pGbu9AROSRZAtaYWFhUCqV7UZ/Kioq2o02SfR6fYflVSoVQkNDuywj1dmddgMCAnDXXXdh8uTJyMzMhEqlQmZmpq2d5uZmVFdXO9x/ANBqtQgKCrJ7UP/jjD20gLapRwODFhGRR5ItaGk0GiQkJCA7O9vueHZ2NqZOndrha6ZMmdKu/MGDB5GYmAi1Wt1lGanOnrQrEUKgqakJAJCQkAC1Wm1XT1lZGU6fPn3HeoiMTgpa0uvrm80wmS297hcRETlX7xaI9FJGRgbS0tKQmJiIKVOmYMeOHSgpKUF6ejoA6zRbaWkpdu3aBQBIT0/H5s2bkZGRgUWLFiE3NxeZmZnYs2ePrc5ly5Zh2rRp2LRpE+bOnYv3338fhw4dwvHjxx1ut76+Hi+99BLmzJmDiIgIVFVVYevWrbhy5Qp+9rOfAQB0Oh1+/etfY+XKlQgNDUVISAhWrVqFcePG4ZFHHnHXKSQvZWx0zmL4IN+2X+HaxhaE9PAG1URE5BqyBq3U1FRUVVXhxRdfRFlZGeLi4nDgwAFER0cDsI4Q3bq3VUxMDA4cOIAVK1Zgy5YtiIyMxBtvvIHHH3/cVmbq1KnYu3cvnn32WTz33HMYOXIk9u3bh0mTJjncrlKpxDfffIO//OUvqKysRGhoKO677z4cO3YMY8eOtdXz6quvQqVSYf78+WhoaMDDDz+Mt99+G0ql0tWnjrycs6YOVUofDNCqUNfUAkODiUGLiMjDKIS0mpxkYTQaodPpYDAYuF6rH/lffz6B499X4tXU8XhswtBe1XX/xk9RWtOAv/3H/bg3aqBzOkhERF1y9Ptb9qsOifojZ41oAVwQT0TkyRi0iGTg1KDVuk6LQYuIyPMwaBHJwLYYvpf7aAHcS4uIyJMxaBG5mcUinLa9w611cESLiMjzMGgRuVl9cwssrZeg9HZ7B4AjWkREnoxBi8jNpJEnjcoHvurebwXCES0iIs/FoEXkZs5cCA8AOn8GLSIiT8WgReRmxoYWAPa7uveGtKCeQYuIyPMwaBG5mdNHtKQ1Wo0MWkREnoZBi8jNnHWfQwk3LCUi8lwMWkRu5sytHW6tx3CTQYuIyNMwaBG5maumDmubWmCx8NalRESehEGLyM2kES1n7AoPAEF+1kX1QgC1jS1OqZOIiJyDQYvIzZw9oqVVKeGr9rGrm4iIPAODFpGbGVtHnZwVtG6ti1ceEhF5FgYtIjeTRp2kKT9n4O7wRESeiUGLyM3agpbzR7QYtIiIPAuDFpGbOXsxPMCgRUTkqRi0iNzM2YvhAW5aSkTkqRi0iNyo0WRGU4sFQNvNoJ2B9zskIvJMDFpEbiRdFahQAAM0zl8Mb2TQIiLyKAxaRG506/osHx+F0+rlGi0iIs/EoEXkRoYG6x5aztzaAWDQIiLyVAxaRG7k7BtKSzh1SETkmRi0iNxIWqPl9KDlzxEtIiJPxKBF5EYGF+yhdWt9DFpERJ6FQYvIjQw3XTx12NgCIYRT6yYiop5j0CJyI2nq0Jm33wHagpbZIlDfbHZq3URE1HMMWkRuZJSuOvR17lWHvmofaJTWX2dOHxIReQ4GLSI3ctVieIVC0XYbnpsMWkREnoJBi8iNXDV1CAC61r25OKJFROQ5GLSI3Kht6tD5QYs3liYi8jwMWkRu1Dai5dw1WsCtVx4yaBEReQrZg9bWrVsRExMDX19fJCQk4NixY12WP3r0KBISEuDr64sRI0Zg+/bt7cpkZWVhzJgx0Gq1GDNmDPbv39+tdk0mE1avXo1x48YhICAAkZGR+OUvf4mrV6/a1fHAAw9AoVDYPRYsWNDDM0H9gdFF+2gB3B2eiMgTyRq09u3bh+XLl+OZZ55BQUEBkpKSMHPmTJSUlHRYvqioCLNmzUJSUhIKCgqwbt06LF26FFlZWbYyubm5SE1NRVpaGgoLC5GWlob58+fjxIkTDrd78+ZN5Ofn47nnnkN+fj7ee+89fPfdd5gzZ067Pi1atAhlZWW2x5tvvunks0R9hRACxkbr1GGgC4MWpw6JiDyHQsi4u+GkSZMQHx+Pbdu22Y6NHj0a8+bNw4YNG9qVX716NT744AOcO3fOdiw9PR2FhYXIzc0FAKSmpsJoNOKjjz6ylZkxYwaCg4OxZ8+eHrULAF9++SUmTpyI4uJiDBs2DIB1ROvee+/Fa6+91uNzYDQaodPpYDAYEBQU1ON6yPPVN7Vg7Pp/AgDOvpgCf41zpw//dPBb/H+ffo9fTonGi3PjnFo3ERHZc/T7W7YRrebmZuTl5SE5OdnueHJyMnJycjp8TW5ubrvyKSkpOHnyJEwmU5dlpDp70i4AGAwGKBQKDBw40O747t27ERYWhrFjx2LVqlWora3t/E0DaGpqgtFotHtQ/1DbOpql8lHAT610ev0c0SIi8jzOX5HroMrKSpjNZoSHh9sdDw8PR3l5eYevKS8v77B8S0sLKisrERER0WkZqc6etNvY2Ig1a9bgF7/4hV1qfeKJJxATEwO9Xo/Tp09j7dq1KCwsRHZ2dqfve8OGDXjhhRc6fZ76rlu3dlAoFE6vX1r3xTVaRESeQ7agJbn9C0cI0eWXUEflbz/uSJ2OtmsymbBgwQJYLBZs3brV7rlFixbZ/jsuLg6jRo1CYmIi8vPzER8f32H/165di4yMDNvPRqMRUVFRHZalvqVtIbxrfu24vQMRkeeRLWiFhYVBqVS2G0WqqKhoN9ok0ev1HZZXqVQIDQ3tsoxUZ3faNZlMmD9/PoqKivDpp5/ecQ1VfHw81Go1zp8/32nQ0mq10Gq1XdZDfZMrNysFOHVIROSJZFujpdFokJCQ0G6aLTs7G1OnTu3wNVOmTGlX/uDBg0hMTIRare6yjFSno+1KIev8+fM4dOiQLch15cyZMzCZTIiIiLhjWep/XLlZKdC2N5d0ZSMREclP1qnDjIwMpKWlITExEVOmTMGOHTtQUlKC9PR0ANZpttLSUuzatQuA9QrDzZs3IyMjA4sWLUJubi4yMzNtVxMCwLJlyzBt2jRs2rQJc+fOxfvvv49Dhw7h+PHjDrfb0tKCn/70p8jPz8eHH34Is9lsGwELCQmBRqPBhQsXsHv3bsyaNQthYWE4e/YsVq5ciQkTJuD+++931ykkL+LKzUoBjmgREXkiWYNWamoqqqqq8OKLL6KsrAxxcXE4cOAAoqOjAQBlZWV2e2rFxMTgwIEDWLFiBbZs2YLIyEi88cYbePzxx21lpk6dir179+LZZ5/Fc889h5EjR2Lfvn2YNGmSw+1euXIFH3zwAQDg3nvvtevz4cOH8cADD0Cj0eCTTz7B66+/jrq6OkRFReHRRx/F+vXroVQ6/4oy8n6u3KwUaAtazS0WNJrM8HXBlY1ERNQ9su6jRdxHqz95+cA57PjsIv592gismzXa6fULITBy3QFYBHBi3cMID/J1ehtERGTl8ftoEfU30ohWoNY1A8kKhcK20J5bPBAReQYGLSI3cfVVhwDXaREReRoGLSI3kXaGd9VieIBBi4jI0zBoEbmJqxfDAwxaRESehkGLyE2MthEt1wUt3oaHiMizMGgRuYk7RrTabsPDTUuJiDwBgxaRGwghXL5hKcCpQyIiT8OgReQGjSYLTGbrlnWuHdGyhjgGLSIiz8CgReQG0miW0kcBf43rdmyXRrSk9oiISF4MWkRu0LY+SwWFQuGydjh1SETkWRi0iNxAGmEKdOG0IXDLiBaDFhGRR2DQInIDY4PrNysFuL0DEZGnYdAicgPbFYduGtHi1CERkWdg0CJyA9tmpW4KWvXNZpjMFpe2RUREd8agReQGtsXwLp46DPRtq5/Th0RE8mPQInIDd00dqpQ+GKBVtbbJ3eGJiOTGoEXkBm2L4V0btACu0yIi8iQMWkRu0Dai5dqpQ+DW+x0yaBERya1HQauoqMjZ/SDq09rWaLl+REsKcwxaRETy61HQuuuuu/Dggw/iv//7v9HY2OjsPhH1OdJ6KVdvWApw01IiIk/So6BVWFiICRMmYOXKldDr9Vi8eDH+9a9/ObtvRH1GbYP7pg65RouIyHP0KGjFxcXhlVdeQWlpKXbu3Iny8nL88Ic/xNixY/HKK6/g+vXrzu4nkVez7aPlxsXwHNEiIpJfrxbDq1QqPPbYY/if//kfbNq0CRcuXMCqVaswdOhQ/PKXv0RZWZmz+knk1WyL4d2xRksKWo0MWkREcutV0Dp58iR+97vfISIiAq+88gpWrVqFCxcu4NNPP0VpaSnmzp3rrH4Sea1GkxnNLdZd2jl1SETUv/Tob/1XXnkFO3fuxLfffotZs2Zh165dmDVrFnx8rLktJiYGb775JmJjY53aWSJvJI0s+SiAAA2DFhFRf9Kjv/W3bduGf/u3f8OTTz4JvV7fYZlhw4YhMzOzV50j6gukzUoDfdXw8VG4vD0GLSIiz9GjoJWdnY1hw4bZRrAkQghcvnwZw4YNg0ajwcKFC53SSSJv1rY+y/WjWbe2IwU8IiKST4/WaI0cORKVlZXtjt+4cQMxMTG97hRRX2LbrNQNe2gBHNEiIvIkPQpaQogOj9fV1cHX17dXHSLqa2xbO7gpaN161aHF0vHvKhERuUe35jIyMjIAAAqFAr///e/h7+9ve85sNuPEiRO49957ndpBIm8njWgFuuGKQ6At0AkB1Da12Ea4iIjI/br1N39BQQEA64jWqVOnoNFobM9pNBqMHz8eq1atcm4PibycO/fQAgBftRJalQ+aWiwwNpgYtIiIZNStoHX48GEAwJNPPonXX38dQUFBLukUUV9S6+apQ8C6TquitgmGBhOi3NYqERHdrkdrtHbu3Om0kLV161bExMTA19cXCQkJOHbsWJfljx49ioSEBPj6+mLEiBHYvn17uzJZWVkYM2YMtFotxowZg/3793erXZPJhNWrV2PcuHEICAhAZGQkfvnLX+Lq1at2dTQ1NeGpp55CWFgYAgICMGfOHFy5cqWHZ4L6KttieDdddQjwNjxERJ7C4aD1k5/8BEaj0fbfXT0ctW/fPixfvhzPPPMMCgoKkJSUhJkzZ6KkpKTD8kVFRZg1axaSkpJQUFCAdevWYenSpcjKyrKVyc3NRWpqKtLS0lBYWIi0tDTMnz8fJ06ccLjdmzdvIj8/H8899xzy8/Px3nvv4bvvvsOcOXPs+rN8+XLs378fe/fuxfHjx1FXV4fZs2fDbDY7fA6o73P3YnigbZqSVx4SEclLITq7hPA2Tz75JN544w0EBgbiySef7LLszp07HWp80qRJiI+Px7Zt22zHRo8ejXnz5mHDhg3tyq9evRoffPABzp07ZzuWnp6OwsJC5ObmAgBSU1NhNBrx0Ucf2crMmDEDwcHB2LNnT4/aBYAvv/wSEydORHFxMYYNGwaDwYBBgwbhr3/9K1JTUwEAV69eRVRUFA4cOICUlBSHzoHRaIROp4PBYOBUbB+18P/+C0e/u44//mw8fpow1C1t/tvbX+LTbyqw6fFxSL1vmFvaJCLqTxz9/nZ4LuPW8ORokOpKc3Mz8vLysGbNGrvjycnJyMnJ6fA1ubm5SE5OtjuWkpKCzMxMmEwmqNVq5ObmYsWKFe3KvPbaaz1uFwAMBgMUCgUGDhwIAMjLy4PJZLLrT2RkJOLi4pCTk9Np0GpqakJTU5PtZ2mUkPou22J4N111CHAvLSIiT9GjNVoNDQ24efOm7efi4mK89tprOHjwoMN1VFZWwmw2Izw83O54eHg4ysvLO3xNeXl5h+VbWlpsG6h2VkaqsyftNjY2Ys2aNfjFL35hS63l5eXQaDQIDg52uB4A2LBhA3Q6ne0RFcWlyn1d2xot9y6GBxi0iIjk1qOgNXfuXOzatQsAUFNTg4kTJ+JPf/oT5s6dazcd5wiFwv7eb0KIdsfuVP72447U6Wi7JpMJCxYsgMViwdatW7t4J471f+3atTAYDLbH5cuX71gneTdZ1mj58jY8RESeoEdBKz8/H0lJSQCAd999F3q9HsXFxdi1axfeeOMNh+oICwuDUqlsN/pTUVHRbrRJotfrOyyvUqkQGhraZRmpzu60azKZMH/+fBQVFSE7O9tuDlav16O5uRnV1dUO9x8AtFotgoKC7B7Ut7l7w1KAi+GJiDxFj4LWzZs3ERgYCAA4ePAgfvKTn8DHxweTJ09GcXGxQ3VoNBokJCQgOzvb7nh2djamTp3a4WumTJnSrvzBgweRmJgItVrdZRmpTkfblULW+fPncejQIVuQkyQkJECtVtvVU1ZWhtOnT3faf+p/mlrMaGqxAODUIRFRf9Sjf2Lfdddd+Nvf/obHHnsM//znP22LzysqKro1QpORkYG0tDQkJiZiypQp2LFjB0pKSpCeng7AOs1WWlpqm6ZMT0/H5s2bkZGRgUWLFiE3NxeZmZm2qwkBYNmyZZg2bRo2bdqEuXPn4v3338ehQ4dw/Phxh9ttaWnBT3/6U+Tn5+PDDz+E2Wy2jYCFhIRAo9FAp9Ph17/+NVauXInQ0FCEhIRg1apVGDduHB555JGenFbqg6TNShUKIFDLxfBERP2O6IF33nlHqNVq4ePjI370ox/Zjr/88stixowZ3apry5YtIjo6Wmg0GhEfHy+OHj1qe27hwoVi+vTpduWPHDkiJkyYIDQajRg+fLjYtm1bh/275557hFqtFrGxsSIrK6tb7RYVFQkAHT4OHz5sK9fQ0CCWLFkiQkJChJ+fn5g9e7YoKSnp1vs3GAwCgDAYDN16HXmHCxW1Inr1hyJu/cdubTf3QqWIXv2hePCPh93aLhFRf+Ho97fD+2jdrry8HGVlZRg/fjx8fKwzkP/6178QFBSE2NhYp4TA/oD7aPVtX12uwbwtn2PIQD98vuYht7V7rsyIma8fQ9gADU4++yO3tUtE1F84fR+t2+n1euj1ertjEydO7Gl1RH2SHFs7APZTh+IOV8ISEZHr9Cho1dfXY+PGjfjkk09QUVEBi8Vi9/zFixed0jkibyfHZqVAW7AzmQUaTGb4a9zbPhERWfXob9/f/OY3OHr0KNLS0hAREcF/LRN1QtrHyt0jWgEaJZQ+CpgtAsaGFgYtIiKZ9Ohv348++gj/+Mc/cP/99zu7P0R9StuIlnuDlkKhgM5PjRv1zTA0mKDX+bq1fSIisurRPlrBwcEICQlxdl+I+py2NVruH1HiFg9ERPLrUdD6wx/+gN///vd29zskovakEa1AN49oAW3rwhi0iIjk06N/Zv/pT3/ChQsXEB4ejuHDh9t2ZZfk5+c7pXNE3s62RsvNi+GBtnVhRgYtIiLZ9Ohv/3nz5jm5G0R9kzSapHPzYvhb2+SIFhGRfHoUtNavX+/sfhD1SQxaRET9W4/WaAFATU0N/vznP2Pt2rW4ceMGAOuUYWlpqdM6R+TtjDIGLdvUYSODFhGRXHo0ovX111/jkUcegU6nw6VLl7Bo0SKEhIRg//79KC4utt0Emqi/q5GClj9HtIiI+qMejWhlZGTgV7/6Fc6fPw9f37b9eWbOnInPPvvMaZ0j8mZCCFvIGeincXv7Oi6GJyKSXY+C1pdffonFixe3Oz5kyBCUl5f3ulNEfUF9sxlmi/We7bJMHfpyRIuISG49Clq+vr4wGo3tjn/77bcYNGhQrztF1BdIAUej9IGvusfLIXusbUSrxe1tExGRVY/+9p87dy5efPFFmEzWLxKFQoGSkhKsWbMGjz/+uFM7SOStDDelXeHVstwPlGu0iIjk16Og9cc//hHXr1/H4MGD0dDQgOnTp+Ouu+5CYGAgXnrpJWf3kcgr1TQ0AwB0Mtx+x9ougxYRkdx69A0QFBSE48eP4/Dhw8jLy4PFYkF8fDweeeQRZ/ePyGtJi9AH+rt/ITzQdn/FBpMZzS0WaFTun74kIurvuh20LBYL3n77bbz33nu4dOkSFAoFYmJioNfrIYSQZYqEyBPJuVkpYF0M76MALMI6ujY40PfOLyIiIqfq1j9xhRCYM2cOfvOb36C0tBTjxo3D2LFjUVxcjF/96ld47LHHXNVPIq8jd9Dy8VHY2q65yelDIiI5dGtE6+2338Znn32GTz75BA8++KDdc59++inmzZuHXbt24Ze//KVTO0nkjeQOWoB12rL6pgnV9c2y9YGIqD/r1ojWnj17sG7dunYhCwAeeughrFmzBrt373Za54i8mRS0gmQNWta2qzmiRUQki24Fra+//hozZszo9PmZM2eisLCw150i6guk6To5R7SCWxfi19zkiBYRkRy6FbRu3LiB8PDwTp8PDw9HdXV1rztF1Be03X6HI1pERP1Vt4KW2WyGStX5si6lUomWFu5CTQS0be/gESNaDRzRIiKSQ7cWwwsh8Ktf/QparbbD55uampzSKaK+wLYY3l/OoNV61WE9R7SIiOTQraC1cOHCO5bhFYdEVp5y1SEAVHONFhGRLLoVtHbu3OmqfhD1KRaL8JCgxX20iIjkxHtyELlAXXMLLML6356wRosjWkRE8mDQInIBQ+sIklblA1+1UrZ+8KpDIiJ5MWgRuYAnTBsC9vtoCSFk7QsRUX/EoEXkAp6wtQPQFrRaLAL1zWZZ+0JE1B8xaBG5gKeMaPlplNCqrL/mvN8hEZH7yR60tm7dipiYGPj6+iIhIQHHjh3rsvzRo0eRkJAAX19fjBgxAtu3b29XJisrC2PGjIFWq8WYMWOwf//+brf73nvvISUlBWFhYVAoFPjqq6/a1fHAAw9AoVDYPRYsWNC9E0B9Uo2HBC2AVx4SEclJ1qC1b98+LF++HM888wwKCgqQlJSEmTNnoqSkpMPyRUVFmDVrFpKSklBQUIB169Zh6dKlyMrKspXJzc1Famoq0tLSUFhYiLS0NMyfPx8nTpzoVrv19fW4//77sXHjxi7fw6JFi1BWVmZ7vPnmm708K9QXeMqIFsArD4mI5KQQMq6QnTRpEuLj47Ft2zbbsdGjR2PevHnYsGFDu/KrV6/GBx98gHPnztmOpaeno7CwELm5uQCA1NRUGI1GfPTRR7YyM2bMQHBwMPbs2dPtdi9duoSYmBgUFBTg3nvvtXvugQcewL333ovXXnutx+fAaDRCp9PBYDAgKCiox/WQZ9n08TfYduQCnrx/ONb/eKysfVmwIxdfXLyB1xfci7n3DpG1L0REfYWj39+yjWg1NzcjLy8PycnJdseTk5ORk5PT4Wtyc3PblU9JScHJkydhMpm6LCPV2ZN2u7J7926EhYVh7NixWLVqFWpra7ss39TUBKPRaPegvscTR7Q4dUhE5H7d2hnemSorK2E2mxEeHm53PDw8HOXl5R2+pry8vMPyLS0tqKysRERERKdlpDp70m5nnnjiCcTExECv1+P06dNYu3YtCgsLkZ2d3elrNmzYgBdeeKFb7ZD38aSgxdvwEBHJR7agJVEoFHY/CyHaHbtT+duPO1Jnd9vtyKJFi2z/HRcXh1GjRiExMRH5+fmIj4/v8DVr165FRkaG7Wej0YioqKhutUuez1O2dwBuubE0R7SIiNxOtqnDsLAwKJXKdqNIFRUV7UabJHq9vsPyKpUKoaGhXZaR6uxJu46Kj4+HWq3G+fPnOy2j1WoRFBRk96C+Rwo1nhG02jYtJSIi95ItaGk0GiQkJLSbZsvOzsbUqVM7fM2UKVPalT948CASExOhVqu7LCPV2ZN2HXXmzBmYTCZERET0qh7yfp40dajjbXiIiGQj69RhRkYG0tLSkJiYiClTpmDHjh0oKSlBeno6AOs0W2lpKXbt2gXAeoXh5s2bkZGRgUWLFiE3NxeZmZm2qwkBYNmyZZg2bRo2bdqEuXPn4v3338ehQ4dw/Phxh9sFgBs3bqCkpARXr14FAHz77bcArCNmer0eFy5cwO7duzFr1iyEhYXh7NmzWLlyJSZMmID777/f5eeOPJs0eiTtYSUnjmgREclIyGzLli0iOjpaaDQaER8fL44ePWp7buHChWL69Ol25Y8cOSImTJggNBqNGD58uNi2bVu7Ot955x1xzz33CLVaLWJjY0VWVla32hVCiJ07dwoA7R7r168XQghRUlIipk2bJkJCQoRGoxEjR44US5cuFVVVVd16/waDQQAQBoOhW68jz2VqMYvo1R+K6NUfiuu1jXJ3R3xZVCWiV38okjZ9KndXiIj6DEe/v2XdR4u4j1ZfVFXXhIT/OgQA+P6lmVAp5b0Bw/cVdXjklaMI9FXh1PMpsvaFiKiv8Ph9tIj6KmktVKCvSvaQBbRddVjb2IIWs0Xm3hAR9S/yfwsQ9THSWihpbZTcbl2QL92DkYiI3INBi8jJpBGtYA9YCA8AKqUPAn2t171wLy0iIvdi0CJyMmkH9uAAzxjRAnjlIRGRXBi0iJzM06YOgbbQd6OeQYuIyJ0YtIic7Ea9dXrOE/bQkoQyaBERyYJBi8jJPHFEK6Q1aFUxaBERuRWDFpGT2dZoedKI1oDWoFXHoEVE5E4MWkROJl11ONCDRrTapg6bZO4JEVH/wqBF5GSeOXWoBcCpQyIid2PQInKythEtTh0SEfV3DFpETiSEaBvR8qB9tHjVIRGRPBi0iJyovtkMk9l6n3ZPWgzfdtVhE3gfeSIi92HQInKi6tYRI43KB35qpcy9aRPaukbLZBaobWqRuTdERP0HgxaRE0lbO4T4a6BQKGTuTRs/jRL+Gmvw4zotIiL3YdAiciJPXAgvkRbEc4sHIiL3YdAiciJP3NpBYtvigSNaRERuw6BF5ETSGq3gAA8c0eJteIiI3I5Bi8iJPHFXeAm3eCAicj8GLSInqvHA+xxKQlrXaFXWcY0WEZG7MGgROZE0ouWJa7Q4okVE5H4MWkROJG3v4JlTh9bF8AxaRETuw6BF5ETSFX3SVgqepG3qkEGLiMhdGLSInKiqdY+qsNbRI08SZhvR4hotIiJ3YdAichIhhFeMaN2ob+b9DomI3IRBi8hJjA0taLFYA4x0E2dPIi2GN5kFjI283yERkTswaBE5iTRtOECrgq8H3VBa4qtWYoBWBQC4XsvpQyIid2DQInISacd1T5w2lAwOtK7TYtAiInIPBi0iJ6lq3Qg01AOnDSWDWoNWRW2jzD0hIuofGLSInKRtRMvzrjiUDOKIFhGRWzFoETmJdMVhmEdPHfoCYNAiInIXBi0iJ5GmDj3xikPJ4CBp6pBBi4jIHRi0iJykUpo69MDNSiWDBnDqkIjInWQPWlu3bkVMTAx8fX2RkJCAY8eOdVn+6NGjSEhIgK+vL0aMGIHt27e3K5OVlYUxY8ZAq9VizJgx2L9/f7fbfe+995CSkoKwsDAoFAp89dVX7epoamrCU089hbCwMAQEBGDOnDm4cuVK904A9Rk3PHizUknbiBYXwxMRuYOsQWvfvn1Yvnw5nnnmGRQUFCApKQkzZ85ESUlJh+WLioowa9YsJCUloaCgAOvWrcPSpUuRlZVlK5Obm4vU1FSkpaWhsLAQaWlpmD9/Pk6cONGtduvr63H//fdj48aNnfZ/+fLl2L9/P/bu3Yvjx4+jrq4Os2fPhtlsdsLZIW9ju/2OBy+G5xotIiI3EzKaOHGiSE9PtzsWGxsr1qxZ02H5p59+WsTGxtodW7x4sZg8ebLt5/nz54sZM2bYlUlJSRELFizoUbtFRUUCgCgoKLA7XlNTI9Rqtdi7d6/tWGlpqfDx8REff/xxh/3viMFgEACEwWBw+DXkmeJfPCiiV38ozl713P+XVXVNInr1hyJ69YeiyWSWuztERF7L0e9v2Ua0mpubkZeXh+TkZLvjycnJyMnJ6fA1ubm57cqnpKTg5MmTMJlMXZaR6uxJux3Jy8uDyWSyqycyMhJxcXFd1tPU1ASj0Wj3IO9ntgjcuOn5U4fB/mqolQoAQGUdR7WIiFxNtqBVWVkJs9mM8PBwu+Ph4eEoLy/v8DXl5eUdlm9paUFlZWWXZaQ6e9JuZ33RaDQIDg7uVj0bNmyATqezPaKiohxukzxXzc1mSPdpDvH33KClUChsC+J55SERkevJvhheoVDY/SyEaHfsTuVvP+5Ind1t11F3qmft2rUwGAy2x+XLl3vdJslP2qw02F8NlVL2X6su2XaHN3JBPBGRq8n2jRAWFgalUtlu9KeioqLdaJNEr9d3WF6lUiE0NLTLMlKdPWm3s740Nzejurq6W/VotVoEBQXZPcj7VXrBHlqSQdKCeE4dEhG5nGxBS6PRICEhAdnZ2XbHs7OzMXXq1A5fM2XKlHblDx48iMTERKjV6i7LSHX2pN2OJCQkQK1W29VTVlaG06dPd6se6huq6jz/9jsS2xYPRgYtIiJXU8nZeEZGBtLS0pCYmIgpU6Zgx44dKCkpQXp6OgDrNFtpaSl27doFAEhPT8fmzZuRkZGBRYsWITc3F5mZmdizZ4+tzmXLlmHatGnYtGkT5s6di/fffx+HDh3C8ePHHW4XAG7cuIGSkhJcvXoVAPDtt98CsI5k6fV66HQ6/PrXv8bKlSsRGhqKkJAQrFq1CuPGjcMjjzzi8nNHnuVGvefffkdi27SUI1pERK7n+gsgu7ZlyxYRHR0tNBqNiI+PF0ePHrU9t3DhQjF9+nS78keOHBETJkwQGo1GDB8+XGzbtq1dne+884645557hFqtFrGxsSIrK6tb7QohxM6dOwWAdo/169fbyjQ0NIglS5aIkJAQ4efnJ2bPni1KSkq69f65vUPf8Kd/fiOiV38ontn/tdxduaP//uKSiF79ofj121/K3RUiIq/l6Pe3QgjpWimSg9FohE6ng8Fg4HotL7Ym62vs/fIyVjxyN5Y9Mkru7nQp++w1LNp1EuOH6vD+kh/K3R0iIq/k6Pe3Z18eReQlpK0SpPVPnsx21SG3dyAicjkGLSInkO4dODjQ84NWhM561WFFbRPMFg5oExG5EoMWkRNIV/BJ9xL0ZGEDtFD5KGC2CN7zkIjIxRi0iHrJbBG2fbS8YepQ6aNAeJA1EF41NMjcGyKivo1Bi6iXquqbYBGAQgGEesGGpQCgb50+LDdwd3giIldi0CLqJWn6LTRA6/G335FI67Su1nBEi4jIlbzjW4HIg9muOPSChfCSCI5oERG5BYMWUS9dN3rP+ixJhM4PAFDGoEVE5FIMWkS95E1bO0ikEa0yLoYnInIpBi2iXmqbOvT8rR0kelvQ4ogWEZErMWgR9VKFF04dRg60Th1W1DahxWyRuTdERH0XgxZRL0lTh4MGeE/Qstu0tI6blhIRuQqDFlEvedN9DiV2m5bWcPqQiMhVGLSIesFiEbhmtAYVfeuVfN4icqA1aJVyLy0iIpdh0CLqhar6ZpjMAj4K77rqEACigv0BAJdv3JS5J0REfReDFlEvSNsjDArUQu0lu8JLhoZYg9aVao5oERG5ind9MxB5GGl7hAgvmzYEgKHB1j5fqeaIFhGRqzBoEfVCWev6JmkDUG/CqUMiItdj0CLqhTKj945oRYVY+1xa0wCLRcjcGyKivolBi6gXymqkoOV9I1r6IF8ofRQwmQWu1XKLByIiV2DQIuqFcmmN1kDvC1oqpY9ti4fLN7ggnojIFRi0iHrhqsF712gBwNCB0pWHXKdFROQKDFpEPXTrZqXeuEYLaFunxREtIiLXYNAi6qHK+iav3axUIl15WMIrD4mIXIJBi6iHpIXwgwK1UHnZZqWS4WEBAIBLVfUy94SIqG/yzm8HIg8g7ag+tHVUyBvFtAatokoGLSIiV2DQIuqhy60LyKOCvXN9FtAWtG7UN8Nw0yRzb4iI+h4GLaIeknZUjwrx3hGtAK0K4UHW9WVFnD4kInI6Bi2iHrrcOnUY5cVTh8Ct04d1MveEiKjvYdAi6qErrSNaQ0O8d+oQAGLCBgAAiq5zRIuIyNkYtIh6wGIRtsXw3j6iNaJ1ROsiF8QTETkdgxZRD1TUNqHZbIHSR+G1u8JLeOUhEZHryB60tm7dipiYGPj6+iIhIQHHjh3rsvzRo0eRkJAAX19fjBgxAtu3b29XJisrC2PGjIFWq8WYMWOwf//+brcrhMDzzz+PyMhI+Pn54YEHHsCZM2fsyjzwwANQKBR2jwULFvTgLJC3kTb4jBzo67V7aEliBrUFLYtFyNwbIqK+RdZviH379mH58uV45plnUFBQgKSkJMycORMlJSUdli8qKsKsWbOQlJSEgoICrFu3DkuXLkVWVpatTG5uLlJTU5GWlobCwkKkpaVh/vz5OHHiRLfa/d//+3/jlVdewebNm/Hll19Cr9fjRz/6EWpra+36tGjRIpSVldkeb775ppPPEnki2xWHXj5tCADDQvyhUfrgZrMZpTW8FQ8RkVMJGU2cOFGkp6fbHYuNjRVr1qzpsPzTTz8tYmNj7Y4tXrxYTJ482fbz/PnzxYwZM+zKpKSkiAULFjjcrsViEXq9XmzcuNH2fGNjo9DpdGL79u22Y9OnTxfLli1z4J12zmAwCADCYDD0qh5yr1ezvxXRqz8UT79TKHdXnCLl1aMievWHIvtMudxdISLyCo5+f8s2otXc3Iy8vDwkJyfbHU9OTkZOTk6Hr8nNzW1XPiUlBSdPnoTJZOqyjFSnI+0WFRWhvLzcroxWq8X06dPb9W337t0ICwvD2LFjsWrVqnYjXrdramqC0Wi0e5D3KamyjmgNC/X+ES0AiNUHAgC+vdb155eIiLpHJVfDlZWVMJvNCA8PtzseHh6O8vLyDl9TXl7eYfmWlhZUVlYiIiKi0zJSnY60K/3ZUZni4mLbz0888QRiYmKg1+tx+vRprF27FoWFhcjOzu70fW/YsAEvvPBCp8+Td7jQunBcumLP290tBa1yBi0iImeSLWhJFAqF3c9CiHbH7lT+9uOO1OmMMosWLbL9d1xcHEaNGoXExETk5+cjPj6+w/6vXbsWGRkZtp+NRiOioqI6LEueSQiBi9etm3uOGDRA5t44hzSi9R1HtIiInEq2qcOwsDAolcp2o1cVFRXtRpIker2+w/IqlQqhoaFdlpHqdKRdvV4PAN3qGwDEx8dDrVbj/PnznZbRarUICgqye5B3qaxrRm1jCxQKILqPTB3eHW4NWheu18FktsjcGyKivkO2oKXRaJCQkNBumi07OxtTp07t8DVTpkxpV/7gwYNITEyEWq3usoxUpyPtStOBt5Zpbm7G0aNHO+0bAJw5cwYmkwkRERFdvXXyctJo1tBgP/iqlTL3xjmGDPTDAK0KJrPgflpERE4k69RhRkYG0tLSkJiYiClTpmDHjh0oKSlBeno6AOs0W2lpKXbt2gUASE9Px+bNm5GRkYFFixYhNzcXmZmZ2LNnj63OZcuWYdq0adi0aRPmzp2L999/H4cOHcLx48cdblehUGD58uV4+eWXMWrUKIwaNQovv/wy/P398Ytf/AIAcOHCBezevRuzZs1CWFgYzp49i5UrV2LChAm4//773XUKSQYXbeuz+sa0IWD9zN+jD0RecTXOXjXaRriIiKh3ZA1aqampqKqqwosvvoiysjLExcXhwIEDiI6OBgCUlZXZ7W0VExODAwcOYMWKFdiyZQsiIyPxxhtv4PHHH7eVmTp1Kvbu3Ytnn30Wzz33HEaOHIl9+/Zh0qRJDrcLAE8//TQaGhrwu9/9DtXV1Zg0aRIOHjyIwEDrF5BGo8Enn3yC119/HXV1dYiKisKjjz6K9evXQ6nsG6Mc1LG29Vl9YyG8ZNwQHfKKq/H1FQPmTRgid3eIiPoEhZBWk5MsjEYjdDodDAYD12t5iV+//SU++aYCf5gXh7TJ0Xd+gZfIyruCle8U4r7hwXgnvfMpciIicvz727vvHUIkA2nqcGQf2dpBMj5KBwA4XWpECxfEExE5BYMWUTc0msworrIGrbsG9501WgAQEzYAARolGkxmXLjOBfFERM7AoEXUDd9dq4VFACEBGgwK1MrdHadS+igQN8Q6qlV4pUbezhAR9REMWkTdcK7Mesuk0RGBXW6s661+MNQatL5m0CIicgoGLaJuOFdm3Tl9tL5vXrgwYVgwAODkpWqZe0JE1DcwaBF1gzSiFRvRN4PWfcNDAADflNei5mazzL0hIvJ+DFpEDhJC2E0d9kWDArUY2bo/2L+KbsjcGyIi78egReSgMkMjjI0tUPko+twVh7eaNMJ639ATDFpERL3GoEXkoDNXraNZIwcNgFbVd3f/nxRjnT48UVQlc0+IiLwfgxaRgwpKrAvEpY09+6pJMdYRrbNXjVynRUTUSwxaRA4qKKkB0HZlXl+l1/ninvBAWARw9LvrcneHiMirMWgROaDFbLFt4hnfx4MWADwYOxgA8Ok3FTL3hIjIuzFoETngu2t1uNlsxgCtqk8vhJc8PNoatI5+dx1mC+87T0TUUwxaRA7Ib12fdW/UQCh9+t6O8LebEDUQOj81am6abO+diIi6j0GLyAH5xdawMWHYQHk74iYqpQ8eap0+/MfXZTL3hojIezFoEd2BEALHv68EAExp3WOqP5gzPhIA8PfCqzCZLTL3hojIOzFoEd3Bd9fqUFHbBF+1DxKG9/2F8JIfjgpDSIAGVfXNtqBJRETdw6BFdAfHzlu3OJgUE9qnNyq9nVrpgx//IAIAsD+/VObeEBF5JwYtojs4dt46mpM0KkzmnrjfTxOiAAAfnS5DhbFR5t4QEXkfBi2iLtxsbrHdiuaH/TBojRuqQ0J0MExmgf8+USJ3d4iIvA6DFlEXPv2mAo0mC4aF+OOe8EC5uyOLJ+8fDgD4fyeK0Wgyy9sZIiIvw6BF1IUPC61bGzz6gwgoFH1//6yOzBirx9BgP1TWNePtnEtyd4eIyKswaBF1oq6pBYe/td6CZnbrovD+SKX0wYpH7gYAbD38PQw3TTL3iIjIezBoEXXio1NlaGqxICYsAGMiguTujqzmTRiCu8MHwNjYgj8e/Fbu7hAReQ0GLaIOCCGwK7cYAPDThKH9dtpQovRR4PkfjwUA/PWLYnxxsUrmHhEReQcGLaIO5JdU41SpARqVD34+cZjc3fEIU+8Kw88nWrd7WLHvK1yvbZK5R0REno9Bi6gDb31WBACYd28kQgI0MvfGc6ybNRojwgJQZmjEb/87Dw3NvAqRiKgrKrk7QORp8oqr8fGZcvgogN8kjZC7Ox4l0FeNtxYmYt7mz3GyuBpPvv0v/HnhfRigdf5fJcZGE85fq8PF63W4cL0eZYYG3KhvRs1Nk+3ei0ofBYL9NQgdoEF4kC9GDgrAXYMH4B59kEv6RETUXfybiOgWFovAywfOAbCuzbq7n+6d1ZWRgwbg7X+7Dwv/75f44uINzN18HJt/EY/RvbhgQAiBi5X1yLtUjfwS6+N8RR2E6Fl9PgpgTGQQ7hsegskjQvHDu8IQwOBFRDJQCNHTv8rIGYxGI3Q6HQwGA4KC+veVbZ7gz8cu4r/+cQ5+aiUOr3oAep2v3F3yWIWXa7D4r3koNzZC6aPAE5OG4Tc/HIFhof53fK3FIvBNeS3+VVSFf126gX8V3UBlXXO7cvogX9w1eABGDApAVLA/ggM0CPZXQ6PygRCA2SJQfbMZN+qbcaW6AReu1+G7a7W4ZrRfP6ZR+mDKyFA8MnowHh4djsiBfk47D0TUPzn6/c2gJTMGLc9ReLkGP9uei2azBf81Lw7/a3K03F3yeFV1TVi3/xT+eeYaAEChAOIidZgyMhQxYQEIDdDAR6FAXVMLrtc2ofhGPc6V1eKbMiPqb1vfpVX5YPzQgYiPDkb8MOufYQO0PerX1ZoGnCyuxpdFN/DZ+esorrpp9/y4ITqkjA1Hylg97ho8oN9fVUpAo8mM06UGXKysR4WxERW1TWhoNsNsEbAIgSA/NXR+agwK1GJ4aABiwgIwZKAffHz42emvGLS8BIOWZ/juWi1S38xF9U0THhkdjrd+mcAv327I+b4Sb352EUe/u+7wawI0SiQMD8GkGOtj3FAdtCql0/smhMD3FXU4dK4Cn5y7hrySarspyRFhAUgeq0fK2HCMHzqQX5z9gBACV6obkF9SjYKSGhSUVOPMVSNaLN37OgzUqvCDKB3GDx2Ie6N6948D8j5eE7S2bt2K//N//g/KysowduxYvPbaa0hKSuq0/NGjR5GRkYEzZ84gMjISTz/9NNLT0+3KZGVl4bnnnsOFCxcwcuRIvPTSS3jssce61a4QAi+88AJ27NiB6upqTJo0CVu2bMHYsWNtZZqamrBq1Srs2bMHDQ0NePjhh7F161YMHTrU4ffPoCW/T7+5hmV7v0JtYwvGRw3E7t9M4kLqHqqobcTn31eioKQGV6qti9eFEPDXqDA4SIsInR9GRwRidEQQRoQFQKV0/4XP12ubcOjcNRw8U47Pv69Cc+vCegAID9IieYweKWP1mDQiBGoZ+kfO12gy41SpAfnF0hrAmg63JxkUqEWsPhD6IF+EB/nCX6uEqjV41za2oOamCeXGRlyqrEdx1U27z45keKg/EqJDkBAdjIToYIwaPIDhvY/yiqC1b98+pKWlYevWrbj//vvx5ptv4s9//jPOnj2LYcPa711UVFSEuLg4LFq0CIsXL8bnn3+O3/3ud9izZw8ef/xxAEBubi6SkpLwhz/8AY899hj279+P3//+9zh+/DgmTZrkcLubNm3CSy+9hLfffht33303/uu//gufffYZvv32WwQGWhdI//a3v8Xf//53vP322wgNDcXKlStx48YN5OXlQal07F/mDFry+ba8FpsPf4+/F14FAMQPG4jMhfchmNs59Bu1jSYc+fY6/nmmHEe+vY66phbbc0G+KkyMCUXi8GAkRgcjbogOvmrnj7iRc7WYLbhwvR5fX6nBqVIDCi/XdDhapfJRYExkEOKHBWPCsIGIHxaMocF+Do9kt5gt+O5aHQqv1KDwck2nF3AE+qoQP8wauhKjgzE+aiAvzOgjvCJoTZo0CfHx8di2bZvt2OjRozFv3jxs2LChXfnVq1fjgw8+wLlz52zH0tPTUVhYiNzcXABAamoqjEYjPvroI1uZGTNmIDg4GHv27HGoXSEEIiMjsXz5cqxevRqAdfQqPDwcmzZtwuLFi2EwGDBo0CD89a9/RWpqKgDg6tWriIqKwoEDB5CSkuLQOWDQcg8hBCpqm3Dheh1OXqrGkW8rkF9SA8C6rmjhlOFYN2s0NCqOYPRXTS1m5HxfhX+eKUf22WuoqrdfnK/0UWB4qD/uDg/EqMEDEBXijwidH/Q6X+h1vhwFdROLRcDQYEJVfROu1zbjak0DLlXVo6h1lOn7ijo0mNrv7zYoUGtd+zcsGPHRwRjnguBsaDChoKQaecXWx1eXa3DztrWIPgogJiwAMWHWizyktV6DArUYFKhFiL+GI2BewtHvb9n+ZmhubkZeXh7WrFljdzw5ORk5OTkdviY3NxfJycl2x1JSUpCZmQmTyQS1Wo3c3FysWLGiXZnXXnvN4XaLiopQXl5u15ZWq8X06dORk5ODxYsXIy8vDyaTya5MZGQk4uLikJOT02nQampqQlNT25C10WjssFxvffj1VZy8VG37+dY8LWzHpJ/FbT/jltfZv8pW5rbXdvT6jsqgXRnR4Wu6KoOu2m790yIE6ptbYGxogbHRhOu1Te3+wlP6KPCj0eFY8tBdiBuiA/VvWpUSD8YOxoOxg/HSYwKnSg04eekGvrx0A3nF1aisa8aF6/W4cL0eH3Xweo3SBwN8VQj0VWGA1vrQqHygVvpA5aOw/qlUQOXjA7VSAYVCAWnwRPpabfu57Yu2fZmuv4Q7+53q7Pe8s9/x7rxW3PaLfaffb0d+t5tbLLjZbEaDyYyG1j9vNptRXd98x7VUARol4oboMG6IDuOG6ro9WtVTOj81HrhnMB64ZzAA66jXN+W1yCuuxsniauQXV6O0psH2OcK59nUofRQI8lXBX2P9DPlrlQjQqKBV+UDpo4BKqYDSx/qZUvoobH/e/tZu/QwB6OD5236+w7nx9iWrs38QgYToEFnali1oVVZWwmw2Izw83O54eHg4ysvLO3xNeXl5h+VbWlpQWVmJiIiITstIdTrSrvRnR2WKi4ttZTQaDYKDgx3uPwBs2LABL7zwQqfPO0vOhSr8vxMlLm/Hmyh9FIgK9sOYyCDcf1cYHhkdjvAgbt9A7Sl9FLg3yrrA+TdJIyCEwDVjE767VovvrtXi+4o6lNY0oNzQiHJjI2obW9BstuBGvXWrCXK9IF8VwgK1CA/0xfCwAMSE+WN4aABGDh6AmNAAjxgVUil9EDdEh7ghOiycOhwAUG5oxPcVdSiqrMPFSutIXLmhEZV1Taiqb27dssSE6psmeTvfx4waHNj/gpbk9hQthOgyWXdU/vbjjtTprDK3u1OZtWvXIiMjw/az0WhEVFRUl3X2xAN3D0KIv3Wt0a3dsf1n68Hb/yVtPaawO3bru7Eda3euuvn628rY19VV324r08V7C9SqEOSnQpCvGiEBGgwN9ufUIPWIQqGwTRFOu3tQu+frm1pgaDChrqkFtY0m1Da2WMNXiwVmi4DJYkGLWcBktqDFItBitsBy2yjs7aNG1mOwK9TRaLOA6Px3rpPfpc7Kd/b73a3XdvK73dnvdWe/01qVD3zVSviplfDXqOCnsf4cEqBBaIDWa3+Xpc/RD0eFtXuuxWxBVX0zjK2fpZvNZtQ1taC+qcX22TFbBFrM1j/Nwvqn6bZF+bcvCGo3/ndbgdufb/9679+cYGykfEtzZAtaYWFhUCqV7UZ/Kioq2o0kSfR6fYflVSoVQkNDuywj1elIu3q9HoB11CoiIqLTMs3NzaiurrYb1aqoqMDUqVM7fd9arRZaresv/00eq0fyWL3L2yEiIECr4gJn6jWV0gfhrVc8Ut8h2z8JNBoNEhISkJ2dbXc8Ozu706AyZcqUduUPHjyIxMREqNXqLstIdTrSbkxMDPR6vV2Z5uZmHD161FYmISEBarXarkxZWRlOnz7dZdAiIiKifkTIaO/evUKtVovMzExx9uxZsXz5chEQECAuXbokhBBizZo1Ii0tzVb+4sWLwt/fX6xYsUKcPXtWZGZmCrVaLd59911bmc8//1wolUqxceNGce7cObFx40ahUqnEF1984XC7QgixceNGodPpxHvvvSdOnTolfv7zn4uIiAhhNBptZdLT08XQoUPFoUOHRH5+vnjooYfE+PHjRUtLi8PnwGAwCADCYDD06BwSERGR+zn6/S1r0BJCiC1btojo6Gih0WhEfHy8OHr0qO25hQsXiunTp9uVP3LkiJgwYYLQaDRi+PDhYtu2be3qfOedd8Q999wj1Gq1iI2NFVlZWd1qVwghLBaLWL9+vdDr9UKr1Ypp06aJU6dO2ZVpaGgQS5YsESEhIcLPz0/Mnj1blJSUdOv9M2gRERF5H0e/v2XfGb6/4z5aRERE3sfR72/vvGyDiIiIyAswaBERERG5CIMWERERkYswaBERERG5CIMWERERkYswaBERERG5CIMWERERkYswaBERERG5CIMWERERkYvwdvMykzbmNxqNMveEiIiIHCV9b9/pBjsMWjKrra0FAERFRcncEyIiIuqu2tpa6HS6Tp/nvQ5lZrFYcPXqVQQGBkKhUACwpuSoqChcvny5397/kOeA5wDgOQB4DgCeA4DnAPC8cyCEQG1tLSIjI+Hj0/lKLI5oyczHxwdDhw7t8LmgoCCP+DDJieeA5wDgOQB4DgCeA4DnAPCsc9DVSJaEi+GJiIiIXIRBi4iIiMhFGLQ8kFarxfr166HVauXuimx4DngOAJ4DgOcA4DkAeA4A7z0HXAxPRERE5CIc0SIiIiJyEQYtIiIiIhdh0CIiIiJyEQYtIiIiIhdh0HKhl156CVOnToW/vz8GDhzYYZmSkhL8+Mc/RkBAAMLCwrB06VI0NzfblTl16hSmT58OPz8/DBkyBC+++GK7eysdPXoUCQkJ8PX1xYgRI7B9+/Z2bWVlZWHMmDHQarUYM2YM9u/f77T36qjhw4dDoVDYPdasWWNXxp3nxJNt3boVMTEx8PX1RUJCAo4dOyZ3l3rk+eefb/f/XK/X254XQuD5559HZGQk/Pz88MADD+DMmTN2dTQ1NeGpp55CWFgYAgICMGfOHFy5csWuTHV1NdLS0qDT6aDT6ZCWloaamhp3vMV2PvvsM/z4xz9GZGQkFAoF/va3v9k978737Mjvkyvc6Rz86le/ave5mDx5sl0Zbz4HGzZswH333YfAwEAMHjwY8+bNw7fffmtXpq9/Dhw5B339cwAAEOQyv//978Urr7wiMjIyhE6na/d8S0uLiIuLEw8++KDIz88X2dnZIjIyUixZssRWxmAwiPDwcLFgwQJx6tQpkZWVJQIDA8Uf//hHW5mLFy8Kf39/sWzZMnH27Fnx1ltvCbVaLd59911bmZycHKFUKsXLL78szp07J15++WWhUqnEF1984dJzcLvo6Gjx4osvirKyMtujtrbW9rw7z4kn27t3r1Cr1eKtt94SZ8+eFcuWLRMBAQGiuLhY7q512/r168XYsWPt/p9XVFTYnt+4caMIDAwUWVlZ4tSpUyI1NVVEREQIo9FoK5Oeni6GDBkisrOzRX5+vnjwwQfF+PHjRUtLi63MjBkzRFxcnMjJyRE5OTkiLi5OzJ49263vVXLgwAHxzDPPiKysLAFA7N+/3+55d71nR36f5DoHCxcuFDNmzLD7XFRVVdmV8eZzkJKSInbu3ClOnz4tvvrqK/Hoo4+KYcOGibq6OluZvv45cOQc9PXPgRBCMGi5wc6dOzsMWgcOHBA+Pj6itLTUdmzPnj1Cq9UKg8EghBBi69atQqfTicbGRluZDRs2iMjISGGxWIQQQjz99NMiNjbWru7FixeLyZMn236eP3++mDFjhl2ZlJQUsWDBgl6/v+6Ijo4Wr776aqfPu/OceLKJEyeK9PR0u2OxsbFizZo1MvWo59avXy/Gjx/f4XMWi0Xo9XqxceNG27HGxkah0+nE9u3bhRBC1NTUCLVaLfbu3WsrU1paKnx8fMTHH38shBDi7NmzAoDdPxxyc3MFAPHNN9+44F057vaQ4c737Mjvkzt0FrTmzp3b6Wv62jmoqKgQAMTRo0eFEP3zc3D7ORCif3wOOHUoo9zcXMTFxSEyMtJ2LCUlBU1NTcjLy7OVmT59ut0GbSkpKbh69SouXbpkK5OcnGxXd0pKCk6ePAmTydRlmZycHFe8tS5t2rQJoaGhuPfee/HSSy/ZDd2685x4qubmZuTl5bXrf3Jysiz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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of CLASSIFICATION\n",
    "classification_type_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "Other     2261\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ..?\n",
    "replace_class = list(classification_type_counts[classification_type_counts < 1882].index)\n",
    "\n",
    "# Replace in dataframe\n",
    "for cls in replace_class:\n",
    "    application_df.CLASSIFICATION = application_df.CLASSIFICATION.replace(cls,\"Other\")\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "application_df.CLASSIFICATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable lists\n",
    "application_cat = application_df.dtypes[application_df.dtypes == \"object\"].index.tolist()\n",
    "application_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\trevo\\anaconda3\\envs\\mlenv\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   APPLICATION_TYPE_Other  APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  \\\n",
       "0                     0.0                   1.0                   0.0   \n",
       "1                     0.0                   0.0                   0.0   \n",
       "2                     0.0                   0.0                   0.0   \n",
       "3                     0.0                   0.0                   0.0   \n",
       "4                     0.0                   0.0                   0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T3  APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  1.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  1.0   \n",
       "3                  1.0                  0.0                  0.0   \n",
       "4                  1.0                  0.0                  0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T6  APPLICATION_TYPE_T7  APPLICATION_TYPE_T8  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   AFFILIATION_CompanySponsored  ...  INCOME_AMT_1-9999  \\\n",
       "0                           0.0  ...                0.0   \n",
       "1                           0.0  ...                1.0   \n",
       "2                           1.0  ...                0.0   \n",
       "3                           1.0  ...                0.0   \n",
       "4                           0.0  ...                0.0   \n",
       "\n",
       "   INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  INCOME_AMT_10M-50M  \\\n",
       "0                     0.0                       0.0                 0.0   \n",
       "1                     0.0                       0.0                 0.0   \n",
       "2                     0.0                       0.0                 0.0   \n",
       "3                     1.0                       0.0                 0.0   \n",
       "4                     0.0                       1.0                 0.0   \n",
       "\n",
       "   INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  INCOME_AMT_50M+  \\\n",
       "0               0.0                     0.0              0.0   \n",
       "1               0.0                     0.0              0.0   \n",
       "2               0.0                     0.0              0.0   \n",
       "3               0.0                     0.0              0.0   \n",
       "4               0.0                     0.0              0.0   \n",
       "\n",
       "   INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y  \n",
       "0                0.0                       1.0                       0.0  \n",
       "1                0.0                       1.0                       0.0  \n",
       "2                0.0                       1.0                       0.0  \n",
       "3                0.0                       1.0                       0.0  \n",
       "4                0.0                       1.0                       0.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(application_df[application_cat]))\n",
    "\n",
    "# Add the encoded variable names to the dataframe\n",
    "encode_df.columns = enc.get_feature_names(application_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\trevo\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATUS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
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       "<p>5 rows × 44 columns</p>\n",
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      ],
      "text/plain": [
       "   STATUS  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Other  \\\n",
       "0       1     5000              1                     0.0   \n",
       "1       1   108590              1                     0.0   \n",
       "2       1     5000              0                     0.0   \n",
       "3       1     6692              1                     0.0   \n",
       "4       1   142590              1                     0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  APPLICATION_TYPE_T3  \\\n",
       "0                   1.0                   0.0                  0.0   \n",
       "1                   0.0                   0.0                  1.0   \n",
       "2                   0.0                   0.0                  0.0   \n",
       "3                   0.0                   0.0                  1.0   \n",
       "4                   0.0                   0.0                  1.0   \n",
       "\n",
       "   APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  APPLICATION_TYPE_T6  ...  \\\n",
       "0                  0.0                  0.0                  0.0  ...   \n",
       "1                  0.0                  0.0                  0.0  ...   \n",
       "2                  0.0                  1.0                  0.0  ...   \n",
       "3                  0.0                  0.0                  0.0  ...   \n",
       "4                  0.0                  0.0                  0.0  ...   \n",
       "\n",
       "   INCOME_AMT_1-9999  INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  \\\n",
       "0                0.0                     0.0                       0.0   \n",
       "1                1.0                     0.0                       0.0   \n",
       "2                0.0                     0.0                       0.0   \n",
       "3                0.0                     1.0                       0.0   \n",
       "4                0.0                     0.0                       1.0   \n",
       "\n",
       "   INCOME_AMT_10M-50M  INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  \\\n",
       "0                 0.0               0.0                     0.0   \n",
       "1                 0.0               0.0                     0.0   \n",
       "2                 0.0               0.0                     0.0   \n",
       "3                 0.0               0.0                     0.0   \n",
       "4                 0.0               0.0                     0.0   \n",
       "\n",
       "   INCOME_AMT_50M+  INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  \\\n",
       "0              0.0                0.0                       1.0   \n",
       "1              0.0                0.0                       1.0   \n",
       "2              0.0                0.0                       1.0   \n",
       "3              0.0                0.0                       1.0   \n",
       "4              0.0                0.0                       1.0   \n",
       "\n",
       "   SPECIAL_CONSIDERATIONS_Y  \n",
       "0                       0.0  \n",
       "1                       0.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "application_df = application_df.merge(encode_df,left_index=True, right_index=True)\n",
    "application_df = application_df.drop(application_cat,1)\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\trevo\\anaconda3\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = application_df[\"IS_SUCCESSFUL\"].values\n",
    "X = application_df.drop([\"IS_SUCCESSFUL\"],1).values\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instances\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliverable 2: Compile, Train and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 80)                3520      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 30)                2430      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 31        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5,981\n",
      "Trainable params: 5,981\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
    "number_input_features = len(X_train[0])\n",
    "hidden_nodes_layer1 = 80\n",
    "hidden_nodes_layer2 = 30\n",
    "\n",
    "nn = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
     ]
    }
   ],
   "source": [
    "# Create a Callback that saves the model's weights every 5 epochs.\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "cp_callback = ModelCheckpoint(\n",
    "    filepath=\"checkpoints/weights.{epoch:02d}.hdf5\",\n",
    "    verbose=1,\n",
    "    save_weights_only=True,\n",
    "    save_freq='epoch',\n",
    "    period=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "804/804 [==============================] - 3s 2ms/step - loss: 0.5704 - accuracy: 0.7212\n",
      "Epoch 2/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5548 - accuracy: 0.7288\n",
      "Epoch 3/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5523 - accuracy: 0.7309\n",
      "Epoch 4/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5501 - accuracy: 0.7309\n",
      "Epoch 5/100\n",
      "799/804 [============================>.] - ETA: 0s - loss: 0.5497 - accuracy: 0.7321\n",
      "Epoch 5: saving model to checkpoints\\weights.05.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5495 - accuracy: 0.7323\n",
      "Epoch 6/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.5485 - accuracy: 0.7328\n",
      "Epoch 7/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5473 - accuracy: 0.7341\n",
      "Epoch 8/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5475 - accuracy: 0.7326\n",
      "Epoch 9/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5465 - accuracy: 0.7342\n",
      "Epoch 10/100\n",
      "800/804 [============================>.] - ETA: 0s - loss: 0.5462 - accuracy: 0.7324\n",
      "Epoch 10: saving model to checkpoints\\weights.10.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5463 - accuracy: 0.7324\n",
      "Epoch 11/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5455 - accuracy: 0.7336\n",
      "Epoch 12/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5459 - accuracy: 0.7332\n",
      "Epoch 13/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.5453 - accuracy: 0.7358\n",
      "Epoch 14/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5445 - accuracy: 0.7353\n",
      "Epoch 15/100\n",
      "804/804 [==============================] - ETA: 0s - loss: 0.5446 - accuracy: 0.7355\n",
      "Epoch 15: saving model to checkpoints\\weights.15.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5446 - accuracy: 0.7355\n",
      "Epoch 16/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5443 - accuracy: 0.7348\n",
      "Epoch 17/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5437 - accuracy: 0.7357\n",
      "Epoch 18/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5436 - accuracy: 0.7346\n",
      "Epoch 19/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5428 - accuracy: 0.7355\n",
      "Epoch 20/100\n",
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      "Epoch 20: saving model to checkpoints\\weights.20.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5430 - accuracy: 0.7350\n",
      "Epoch 21/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5423 - accuracy: 0.7355\n",
      "Epoch 22/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5421 - accuracy: 0.7354\n",
      "Epoch 23/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5418 - accuracy: 0.7366\n",
      "Epoch 24/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5423 - accuracy: 0.7370\n",
      "Epoch 25/100\n",
      "781/804 [============================>.] - ETA: 0s - loss: 0.5415 - accuracy: 0.7360\n",
      "Epoch 25: saving model to checkpoints\\weights.25.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5416 - accuracy: 0.7359\n",
      "Epoch 26/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5417 - accuracy: 0.7365\n",
      "Epoch 27/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5415 - accuracy: 0.7358\n",
      "Epoch 28/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5413 - accuracy: 0.7378\n",
      "Epoch 29/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5410 - accuracy: 0.7360\n",
      "Epoch 30/100\n",
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      "Epoch 30: saving model to checkpoints\\weights.30.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5409 - accuracy: 0.7360\n",
      "Epoch 31/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5413 - accuracy: 0.7365\n",
      "Epoch 32/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5408 - accuracy: 0.7375\n",
      "Epoch 33/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5405 - accuracy: 0.7369\n",
      "Epoch 34/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5406 - accuracy: 0.7372\n",
      "Epoch 35/100\n",
      "797/804 [============================>.] - ETA: 0s - loss: 0.5396 - accuracy: 0.7380\n",
      "Epoch 35: saving model to checkpoints\\weights.35.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5399 - accuracy: 0.7376\n",
      "Epoch 36/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5403 - accuracy: 0.7376\n",
      "Epoch 37/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5399 - accuracy: 0.7381\n",
      "Epoch 38/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5399 - accuracy: 0.7371\n",
      "Epoch 39/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5395 - accuracy: 0.7380\n",
      "Epoch 40/100\n",
      "794/804 [============================>.] - ETA: 0s - loss: 0.5391 - accuracy: 0.7366\n",
      "Epoch 40: saving model to checkpoints\\weights.40.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5393 - accuracy: 0.7368\n",
      "Epoch 41/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5391 - accuracy: 0.7381\n",
      "Epoch 42/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5393 - accuracy: 0.7381\n",
      "Epoch 43/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5390 - accuracy: 0.7384\n",
      "Epoch 44/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5381 - accuracy: 0.7388\n",
      "Epoch 45/100\n",
      "790/804 [============================>.] - ETA: 0s - loss: 0.5389 - accuracy: 0.7374\n",
      "Epoch 45: saving model to checkpoints\\weights.45.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5391 - accuracy: 0.7371\n",
      "Epoch 46/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5389 - accuracy: 0.7384\n",
      "Epoch 47/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5383 - accuracy: 0.7375\n",
      "Epoch 48/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5385 - accuracy: 0.7385\n",
      "Epoch 49/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5385 - accuracy: 0.7385\n",
      "Epoch 50/100\n",
      "803/804 [============================>.] - ETA: 0s - loss: 0.5383 - accuracy: 0.7389\n",
      "Epoch 50: saving model to checkpoints\\weights.50.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5383 - accuracy: 0.7390\n",
      "Epoch 51/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5383 - accuracy: 0.7378\n",
      "Epoch 52/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5379 - accuracy: 0.7379\n",
      "Epoch 53/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.5380 - accuracy: 0.7392\n",
      "Epoch 54/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5381 - accuracy: 0.7379\n",
      "Epoch 55/100\n",
      "801/804 [============================>.] - ETA: 0s - loss: 0.5373 - accuracy: 0.7385\n",
      "Epoch 55: saving model to checkpoints\\weights.55.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5375 - accuracy: 0.7382\n",
      "Epoch 56/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5374 - accuracy: 0.7392\n",
      "Epoch 57/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5371 - accuracy: 0.7385\n",
      "Epoch 58/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5381 - accuracy: 0.7385\n",
      "Epoch 59/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5367 - accuracy: 0.7383\n",
      "Epoch 60/100\n",
      "787/804 [============================>.] - ETA: 0s - loss: 0.5368 - accuracy: 0.7392\n",
      "Epoch 60: saving model to checkpoints\\weights.60.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5370 - accuracy: 0.7394\n",
      "Epoch 61/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5372 - accuracy: 0.7386\n",
      "Epoch 62/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5370 - accuracy: 0.7390\n",
      "Epoch 63/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5370 - accuracy: 0.7385\n",
      "Epoch 64/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5370 - accuracy: 0.7389\n",
      "Epoch 65/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "779/804 [============================>.] - ETA: 0s - loss: 0.5373 - accuracy: 0.7377\n",
      "Epoch 65: saving model to checkpoints\\weights.65.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5366 - accuracy: 0.7380\n",
      "Epoch 66/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5363 - accuracy: 0.7393\n",
      "Epoch 67/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5365 - accuracy: 0.7390\n",
      "Epoch 68/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5363 - accuracy: 0.7399\n",
      "Epoch 69/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5362 - accuracy: 0.7390\n",
      "Epoch 70/100\n",
      "778/804 [============================>.] - ETA: 0s - loss: 0.5364 - accuracy: 0.7393\n",
      "Epoch 70: saving model to checkpoints\\weights.70.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5364 - accuracy: 0.7395\n",
      "Epoch 71/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5363 - accuracy: 0.7390\n",
      "Epoch 72/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5363 - accuracy: 0.7385\n",
      "Epoch 73/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5362 - accuracy: 0.7387\n",
      "Epoch 74/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5361 - accuracy: 0.7399\n",
      "Epoch 75/100\n",
      "786/804 [============================>.] - ETA: 0s - loss: 0.5355 - accuracy: 0.7410\n",
      "Epoch 75: saving model to checkpoints\\weights.75.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5359 - accuracy: 0.7404\n",
      "Epoch 76/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5362 - accuracy: 0.7390\n",
      "Epoch 77/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5359 - accuracy: 0.7392\n",
      "Epoch 78/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5360 - accuracy: 0.7395\n",
      "Epoch 79/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5357 - accuracy: 0.7394\n",
      "Epoch 80/100\n",
      "783/804 [============================>.] - ETA: 0s - loss: 0.5350 - accuracy: 0.7399\n",
      "Epoch 80: saving model to checkpoints\\weights.80.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5354 - accuracy: 0.7395\n",
      "Epoch 81/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5359 - accuracy: 0.7403\n",
      "Epoch 82/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5357 - accuracy: 0.7394\n",
      "Epoch 83/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5356 - accuracy: 0.7401\n",
      "Epoch 84/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7402\n",
      "Epoch 85/100\n",
      "786/804 [============================>.] - ETA: 0s - loss: 0.5367 - accuracy: 0.7391\n",
      "Epoch 85: saving model to checkpoints\\weights.85.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5356 - accuracy: 0.7397\n",
      "Epoch 86/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5354 - accuracy: 0.7399\n",
      "Epoch 87/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7403\n",
      "Epoch 88/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7400\n",
      "Epoch 89/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7397\n",
      "Epoch 90/100\n",
      "795/804 [============================>.] - ETA: 0s - loss: 0.5347 - accuracy: 0.7395\n",
      "Epoch 90: saving model to checkpoints\\weights.90.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5349 - accuracy: 0.7392\n",
      "Epoch 91/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5347 - accuracy: 0.7400\n",
      "Epoch 92/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5351 - accuracy: 0.7396\n",
      "Epoch 93/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5350 - accuracy: 0.7404\n",
      "Epoch 94/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5355 - accuracy: 0.7403\n",
      "Epoch 95/100\n",
      "796/804 [============================>.] - ETA: 0s - loss: 0.5355 - accuracy: 0.7403\n",
      "Epoch 95: saving model to checkpoints\\weights.95.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5353 - accuracy: 0.7403\n",
      "Epoch 96/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5349 - accuracy: 0.7397\n",
      "Epoch 97/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5348 - accuracy: 0.7405\n",
      "Epoch 98/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5346 - accuracy: 0.7400\n",
      "Epoch 99/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5353 - accuracy: 0.7402\n",
      "Epoch 100/100\n",
      "804/804 [==============================] - ETA: 0s - loss: 0.5344 - accuracy: 0.7406\n",
      "Epoch 100: saving model to checkpoints\\weights.100.hdf5\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.5344 - accuracy: 0.7406\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn.fit(X_train_scaled,y_train,epochs=100,callbacks=[cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 1s - loss: 0.5602 - accuracy: 0.7292 - 522ms/epoch - 2ms/step\n",
      "Loss: 0.5601726174354553, Accuracy: 0.7292128205299377\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export our model to HDF5 file\n",
    "nn.save(\"AlphabetSoupCharity.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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