AlphabetSoupCharity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliverable 1: Preprocessing the Data for a Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 4,
     "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(\"./Resources/charity_data.csv\")\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
    "application_df = application_df.drop([\"EIN\",\"NAME\"],1)\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of unique values in each column.\n",
    "application_df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "T14        3\n",
       "T25        3\n",
       "T15        2\n",
       "T29        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of APPLICATION_TYPE\n",
    "application_type_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "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 < 500].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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "         ...  \n",
       "C1370        1\n",
       "C4200        1\n",
       "C1283        1\n",
       "C1248        1\n",
       "C2170        1\n",
       "Name: CLASSIFICATION, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at CLASSIFICATION value counts for binning\n",
    "classification_counts = application_df[\"CLASSIFICATION\"].value_counts()\n",
    "classification_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of CLASSIFICATION\n",
    "classification_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ..?\n",
    "replace_class = list(classification_counts[classification_counts < 1500].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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "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_Other</th>\n",
       "      <th>APPLICATION_TYPE_T10</th>\n",
       "      <th>APPLICATION_TYPE_T19</th>\n",
       "      <th>APPLICATION_TYPE_T3</th>\n",
       "      <th>APPLICATION_TYPE_T4</th>\n",
       "      <th>APPLICATION_TYPE_T5</th>\n",
       "      <th>APPLICATION_TYPE_T6</th>\n",
       "      <th>APPLICATION_TYPE_T7</th>\n",
       "      <th>APPLICATION_TYPE_T8</th>\n",
       "      <th>AFFILIATION_CompanySponsored</th>\n",
       "      <th>...</th>\n",
       "      <th>INCOME_AMT_1-9999</th>\n",
       "      <th>INCOME_AMT_10000-24999</th>\n",
       "      <th>INCOME_AMT_100000-499999</th>\n",
       "      <th>INCOME_AMT_10M-50M</th>\n",
       "      <th>INCOME_AMT_1M-5M</th>\n",
       "      <th>INCOME_AMT_25000-99999</th>\n",
       "      <th>INCOME_AMT_50M+</th>\n",
       "      <th>INCOME_AMT_5M-10M</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_N</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>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": 18,
     "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": 19,
   "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>STATUS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "      <th>APPLICATION_TYPE_Other</th>\n",
       "      <th>APPLICATION_TYPE_T10</th>\n",
       "      <th>APPLICATION_TYPE_T19</th>\n",
       "      <th>APPLICATION_TYPE_T3</th>\n",
       "      <th>APPLICATION_TYPE_T4</th>\n",
       "      <th>APPLICATION_TYPE_T5</th>\n",
       "      <th>APPLICATION_TYPE_T6</th>\n",
       "      <th>...</th>\n",
       "      <th>INCOME_AMT_1-9999</th>\n",
       "      <th>INCOME_AMT_10000-24999</th>\n",
       "      <th>INCOME_AMT_100000-499999</th>\n",
       "      <th>INCOME_AMT_10M-50M</th>\n",
       "      <th>INCOME_AMT_1M-5M</th>\n",
       "      <th>INCOME_AMT_25000-99999</th>\n",
       "      <th>INCOME_AMT_50M+</th>\n",
       "      <th>INCOME_AMT_5M-10M</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_N</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>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",
       "      <td>...</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</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",
       "      <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 × 44 columns</p>\n",
       "</div>"
      ],
      "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": 19,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "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=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 24,
   "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",
      "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(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies for checkpoint\n",
    "# Import checkpoint dependencies\n",
    "import os\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# Define the checkpoint path and filenames\n",
    "os.makedirs(\"checkpoints/\",exist_ok=True)\n",
    "checkpoint_path = \"checkpoints/weights.{epoch:02d}.hdf5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Create a callback that saves the model's weights every 5 epochs\n",
    "cp_callback = ModelCheckpoint(\n",
    "    filepath=checkpoint_path,\n",
    "    verbose=1,\n",
    "    save_weights_only=True,\n",
    "    save_freq=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " 62/804 [=>............................] - ETA: 0s - loss: 0.6088 - accuracy: 0.6971   \n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "193/804 [======>.......................] - ETA: 7s - loss: 0.5857 - accuracy: 0.7134 \n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "256/804 [========>.....................] - ETA: 5s - loss: 0.5824 - accuracy: 0.7142\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "386/804 [=============>................] - ETA: 2s - loss: 0.5772 - accuracy: 0.7173\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "447/804 [===============>..............] - ETA: 2s - loss: 0.5753 - accuracy: 0.7183\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "592/804 [=====================>........] - ETA: 0s - loss: 0.5720 - accuracy: 0.7213\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "662/804 [=======================>......] - ETA: 0s - loss: 0.5714 - accuracy: 0.7221\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "735/804 [==========================>...] - ETA: 0s - loss: 0.5709 - accuracy: 0.7222\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "804/804 [==============================] - 4s 4ms/step - loss: 0.5686 - accuracy: 0.7239\n",
      "Epoch 2/100\n",
      " 75/804 [=>............................] - ETA: 0s - loss: 0.5548 - accuracy: 0.7296\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "145/804 [====>.........................] - ETA: 0s - loss: 0.5601 - accuracy: 0.7291\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "216/804 [=======>......................] - ETA: 0s - loss: 0.5621 - accuracy: 0.7264\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "367/804 [============>.................] - ETA: 0s - loss: 0.5596 - accuracy: 0.7290\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "426/804 [==============>...............] - ETA: 0s - loss: 0.5584 - accuracy: 0.7289\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "566/804 [====================>.........] - ETA: 0s - loss: 0.5557 - accuracy: 0.7311\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "633/804 [======================>.......] - ETA: 0s - loss: 0.5559 - accuracy: 0.7305\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "767/804 [===========================>..] - ETA: 0s - loss: 0.5556 - accuracy: 0.7306\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "804/804 [==============================] - 1s 743us/step - loss: 0.5553 - accuracy: 0.7309\n",
      "Epoch 3/100\n",
      " 70/804 [=>............................] - ETA: 0s - loss: 0.5607 - accuracy: 0.7254\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "134/804 [====>.........................] - ETA: 0s - loss: 0.5484 - accuracy: 0.7367\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "276/804 [=========>....................] - ETA: 0s - loss: 0.5481 - accuracy: 0.7357\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "341/804 [===========>..................] - ETA: 0s - loss: 0.5511 - accuracy: 0.7330\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "486/804 [=================>............] - ETA: 0s - loss: 0.5528 - accuracy: 0.7309\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "551/804 [===================>..........] - ETA: 0s - loss: 0.5535 - accuracy: 0.7304\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "672/804 [========================>.....] - ETA: 0s - loss: 0.5513 - accuracy: 0.7317\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "730/804 [==========================>...] - ETA: 0s - loss: 0.5523 - accuracy: 0.7315\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "804/804 [==============================] - 1s 760us/step - loss: 0.5528 - accuracy: 0.7315\n",
      "Epoch 4/100\n",
      " 52/804 [>.............................] - ETA: 0s - loss: 0.5381 - accuracy: 0.7422\n",
      "Epoch 00004: saving model to checkpoints/weights.04.hdf5\n",
      "159/804 [====>.........................] - ETA: 0s - loss: 0.5477 - accuracy: 0.7341\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
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      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
      "804/804 [==============================] - 1s 905us/step - loss: 0.5363 - accuracy: 0.7402\n",
      "Epoch 95/100\n",
      "  1/804 [..............................] - ETA: 1s - loss: 0.5282 - accuracy: 0.8125\n",
      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
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      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
      "804/804 [==============================] - 1s 1000us/step - loss: 0.5362 - accuracy: 0.7406\n",
      "Epoch 96/100\n",
      "  1/804 [..............................] - ETA: 0s - loss: 0.5900 - accuracy: 0.6562\n",
      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
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      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
      "804/804 [==============================] - 1s 1ms/step - loss: 0.5365 - accuracy: 0.7407\n",
      "Epoch 97/100\n",
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      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
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      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
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      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
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      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
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      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
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      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
      "804/804 [==============================] - 1s 943us/step - loss: 0.5364 - accuracy: 0.7407\n",
      "Epoch 98/100\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
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      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
      "804/804 [==============================] - 1s 868us/step - loss: 0.5355 - accuracy: 0.7406\n",
      "Epoch 99/100\n",
      "  1/804 [..............................] - ETA: 0s - loss: 0.4589 - accuracy: 0.7812\n",
      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
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      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
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      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "254/804 [========>.....................] - ETA: 0s - loss: 0.5282 - accuracy: 0.7414\n",
      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
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      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
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      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
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      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
      "669/804 [=======================>......] - ETA: 0s - loss: 0.5332 - accuracy: 0.7420\n",
      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
      "804/804 [==============================] - 1s 754us/step - loss: 0.5356 - accuracy: 0.7405\n",
      "Epoch 100/100\n",
      "  1/804 [..............................] - ETA: 0s - loss: 0.4650 - accuracy: 0.8125\n",
      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
      " 64/804 [=>............................] - ETA: 0s - loss: 0.5498 - accuracy: 0.7329\n",
      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
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      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
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      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
      "403/804 [==============>...............] - ETA: 0s - loss: 0.5409 - accuracy: 0.7374\n",
      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
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      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
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      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
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      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
      "781/804 [============================>.] - ETA: 0s - loss: 0.5361 - accuracy: 0.7402\n",
      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n",
      "804/804 [==============================] - 1s 799us/step - loss: 0.5359 - 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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 0s - loss: 0.5550 - accuracy: 0.7254\n",
      "Loss: 0.5549597144126892, Accuracy: 0.7253644466400146\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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export to hdf5 file\n",
    "nn.save(\"AlphabetSoupCharity.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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