AlphabetSoupCharity_Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "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",
    "#import warnings\n",
    "#warnings.filterwarnings('ignore')\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\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(\"https://static.bc-edx.com/data/dl-1-2/m21/lms/starter/charity_data.csv\")\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the non-beneficial ID columns, 'EIN', 'NAME', 'SPECIAL_CONSIDERATIONS', 'STATUS'\n",
    "application_df = application_df.drop(columns=['EIN', 'NAME', 'SPECIAL_CONSIDERATIONS', 'STATUS'])"
   ]
  },
  {
   "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",
       "INCOME_AMT             9\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",
    "unique_values_count = application_df.nunique()\n",
    "unique_values_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE\n",
       "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: count, 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": [
       "APPLICATION_TYPE\n",
       "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: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a cutoff value and create a list of application types to be replaced\n",
    "# use the variable name `application_types_to_replace`\n",
    "cutoff_value = 500\n",
    "\n",
    "# Create a list of application types to be replaced\n",
    "application_types_to_replace = application_df['APPLICATION_TYPE'].value_counts()[application_df['APPLICATION_TYPE'].value_counts() < cutoff_value].index.tolist()\n",
    "\n",
    "# Replace in dataframe\n",
    "for app in application_types_to_replace:\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLASSIFICATION\n",
       "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: count, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLASSIFICATION\n",
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "C2800       95\n",
       "C7100       75\n",
       "C1300       58\n",
       "C1280       50\n",
       "C1230       36\n",
       "C1400       34\n",
       "C7200       32\n",
       "C2300       32\n",
       "C1240       30\n",
       "C8000       20\n",
       "C7120       18\n",
       "C1500       16\n",
       "C1800       15\n",
       "C6000       15\n",
       "C1250       14\n",
       "C8200       11\n",
       "C1238       10\n",
       "C1278       10\n",
       "C1235        9\n",
       "C1237        9\n",
       "C7210        7\n",
       "C2400        6\n",
       "C1720        6\n",
       "C4100        6\n",
       "C1257        5\n",
       "C1600        5\n",
       "C1260        3\n",
       "C2710        3\n",
       "C0           3\n",
       "C3200        2\n",
       "C1234        2\n",
       "C1246        2\n",
       "C1267        2\n",
       "C1256        2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You may find it helpful to look at CLASSIFICATION value counts >1\n",
    "classification_counts_greater_than_one = classification_counts[classification_counts > 1]\n",
    "classification_counts_greater_than_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CLASSIFICATION\n",
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "Other      669\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a cutoff value and create a list of classifications to be replaced\n",
    "# use the variable name `classifications_to_replace`\n",
    "classification_cutoff_value = 100\n",
    "\n",
    "# Create a list of classifications to be replaced\n",
    "classifications_to_replace = classification_counts[classification_counts < classification_cutoff_value].index.tolist()\n",
    "\n",
    "# Replace in dataframe\n",
    "for cls in classifications_to_replace:\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>APPLICATION_TYPE_T7</th>\n",
       "      <th>...</th>\n",
       "      <th>ORGANIZATION_Trust</th>\n",
       "      <th>INCOME_AMT_0</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>142590</td>\n",
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       "    <tr>\n",
       "      <th>34294</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34295</th>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34296</th>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34297</th>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34298</th>\n",
       "      <td>36500179</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34299 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Other  APPLICATION_TYPE_T10  \\\n",
       "0          5000              1                   False                  True   \n",
       "1        108590              1                   False                 False   \n",
       "2          5000              0                   False                 False   \n",
       "3          6692              1                   False                 False   \n",
       "4        142590              1                   False                 False   \n",
       "...         ...            ...                     ...                   ...   \n",
       "34294      5000              0                   False                 False   \n",
       "34295      5000              0                   False                 False   \n",
       "34296      5000              0                   False                 False   \n",
       "34297      5000              1                   False                 False   \n",
       "34298  36500179              0                   False                 False   \n",
       "\n",
       "       APPLICATION_TYPE_T19  APPLICATION_TYPE_T3  APPLICATION_TYPE_T4  \\\n",
       "0                     False                False                False   \n",
       "1                     False                 True                False   \n",
       "2                     False                False                False   \n",
       "3                     False                 True                False   \n",
       "4                     False                 True                False   \n",
       "...                     ...                  ...                  ...   \n",
       "34294                 False                False                 True   \n",
       "34295                 False                False                 True   \n",
       "34296                 False                 True                False   \n",
       "34297                 False                False                False   \n",
       "34298                 False                 True                False   \n",
       "\n",
       "       APPLICATION_TYPE_T5  APPLICATION_TYPE_T6  APPLICATION_TYPE_T7  ...  \\\n",
       "0                    False                False                False  ...   \n",
       "1                    False                False                False  ...   \n",
       "2                     True                False                False  ...   \n",
       "3                    False                False                False  ...   \n",
       "4                    False                False                False  ...   \n",
       "...                    ...                  ...                  ...  ...   \n",
       "34294                False                False                False  ...   \n",
       "34295                False                False                False  ...   \n",
       "34296                False                False                False  ...   \n",
       "34297                 True                False                False  ...   \n",
       "34298                False                False                False  ...   \n",
       "\n",
       "       ORGANIZATION_Trust  INCOME_AMT_0  INCOME_AMT_1-9999  \\\n",
       "0                   False          True              False   \n",
       "1                   False         False               True   \n",
       "2                   False          True              False   \n",
       "3                    True         False              False   \n",
       "4                    True         False              False   \n",
       "...                   ...           ...                ...   \n",
       "34294               False          True              False   \n",
       "34295               False          True              False   \n",
       "34296               False          True              False   \n",
       "34297               False          True              False   \n",
       "34298               False         False              False   \n",
       "\n",
       "       INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  INCOME_AMT_10M-50M  \\\n",
       "0                       False                     False               False   \n",
       "1                       False                     False               False   \n",
       "2                       False                     False               False   \n",
       "3                        True                     False               False   \n",
       "4                       False                      True               False   \n",
       "...                       ...                       ...                 ...   \n",
       "34294                   False                     False               False   \n",
       "34295                   False                     False               False   \n",
       "34296                   False                     False               False   \n",
       "34297                   False                     False               False   \n",
       "34298                   False                     False               False   \n",
       "\n",
       "       INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  INCOME_AMT_50M+  \\\n",
       "0                 False                   False            False   \n",
       "1                 False                   False            False   \n",
       "2                 False                   False            False   \n",
       "3                 False                   False            False   \n",
       "4                 False                   False            False   \n",
       "...                 ...                     ...              ...   \n",
       "34294             False                   False            False   \n",
       "34295             False                   False            False   \n",
       "34296             False                   False            False   \n",
       "34297             False                   False            False   \n",
       "34298              True                   False            False   \n",
       "\n",
       "       INCOME_AMT_5M-10M  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                  False  \n",
       "4                  False  \n",
       "...                  ...  \n",
       "34294              False  \n",
       "34295              False  \n",
       "34296              False  \n",
       "34297              False  \n",
       "34298              False  \n",
       "\n",
       "[34299 rows x 47 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical data to numeric with `pd.get_dummies`\n",
    "application_df_encoded = pd.get_dummies(application_df)\n",
    "application_df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "X = application_df_encoded.drop(columns=['IS_SUCCESSFUL'])\n",
    "y = application_df_encoded['IS_SUCCESSFUL']\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,\n",
    "                                                                                y,\n",
    "                                                                                random_state=42,\n",
    "                                                                                stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASK_AMT                         int64\n",
      "APPLICATION_TYPE_Other           bool\n",
      "APPLICATION_TYPE_T10             bool\n",
      "APPLICATION_TYPE_T19             bool\n",
      "APPLICATION_TYPE_T3              bool\n",
      "APPLICATION_TYPE_T4              bool\n",
      "APPLICATION_TYPE_T5              bool\n",
      "APPLICATION_TYPE_T6              bool\n",
      "APPLICATION_TYPE_T7              bool\n",
      "APPLICATION_TYPE_T8              bool\n",
      "AFFILIATION_CompanySponsored     bool\n",
      "AFFILIATION_Family/Parent        bool\n",
      "AFFILIATION_Independent          bool\n",
      "AFFILIATION_National             bool\n",
      "AFFILIATION_Other                bool\n",
      "AFFILIATION_Regional             bool\n",
      "CLASSIFICATION_C1000             bool\n",
      "CLASSIFICATION_C1200             bool\n",
      "CLASSIFICATION_C1270             bool\n",
      "CLASSIFICATION_C1700             bool\n",
      "CLASSIFICATION_C2000             bool\n",
      "CLASSIFICATION_C2100             bool\n",
      "CLASSIFICATION_C2700             bool\n",
      "CLASSIFICATION_C3000             bool\n",
      "CLASSIFICATION_C4000             bool\n",
      "CLASSIFICATION_C5000             bool\n",
      "CLASSIFICATION_C7000             bool\n",
      "CLASSIFICATION_Other             bool\n",
      "USE_CASE_CommunityServ           bool\n",
      "USE_CASE_Heathcare               bool\n",
      "USE_CASE_Other                   bool\n",
      "USE_CASE_Preservation            bool\n",
      "USE_CASE_ProductDev              bool\n",
      "ORGANIZATION_Association         bool\n",
      "ORGANIZATION_Co-operative        bool\n",
      "ORGANIZATION_Corporation         bool\n",
      "ORGANIZATION_Trust               bool\n",
      "INCOME_AMT_0                     bool\n",
      "INCOME_AMT_1-9999                bool\n",
      "INCOME_AMT_10000-24999           bool\n",
      "INCOME_AMT_100000-499999         bool\n",
      "INCOME_AMT_10M-50M               bool\n",
      "INCOME_AMT_1M-5M                 bool\n",
      "INCOME_AMT_25000-99999           bool\n",
      "INCOME_AMT_50M+                  bool\n",
      "INCOME_AMT_5M-10M                bool\n",
      "dtype: object\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "# Check data types of X_train and y_train\n",
    "print(X_train.dtypes)\n",
    "print(y_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert boolean data types to integers in X_train and y_train\n",
    "X_train = X_train.astype(int)\n",
    "y_train = y_train.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "## Compile, Train and Evaluate the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,008</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_3 (\u001b[38;5;33mInputLayer\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m3,008\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,233</span> (28.25 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,233\u001b[0m (28.25 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,233</span> (28.25 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,233\u001b[0m (28.25 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the input shape based on the number of features\n",
    "input_shape = X_train_scaled.shape[1]\n",
    "\n",
    "# Create an Input layer\n",
    "input_layer = tf.keras.Input(shape=(input_shape,))\n",
    "\n",
    "# Define the number of units for the hidden layers\n",
    "hidden_units = 64\n",
    "\n",
    "# Add hidden layers\n",
    "hidden_layer1 = tf.keras.layers.Dense(hidden_units, activation='relu')(input_layer)\n",
    "hidden_layer2 = tf.keras.layers.Dense(hidden_units, activation='relu')(hidden_layer1)\n",
    "\n",
    "# Output layer\n",
    "output_layer = tf.keras.layers.Dense(units=1)(hidden_layer2)\n",
    "\n",
    "# Create the model\n",
    "model = tf.keras.Model(inputs=input_layer, outputs=output_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Print the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependency\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# Define the filepath for saving the weights\n",
    "filepath = \"model.weights.h5\"\n",
    "\n",
    "# Create a ModelCheckpoint callback\n",
    "# Save the weights every 5 epochs\n",
    "checkpoint_callback = ModelCheckpoint(filepath, save_weights_only=True, save_best_only=False, save_freq=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7351 - loss: 0.1817 - val_accuracy: 0.7230 - val_loss: 0.1873\n",
      "Epoch 2/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7423 - loss: 0.1786 - val_accuracy: 0.7226 - val_loss: 0.1880\n",
      "Epoch 3/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7382 - loss: 0.1810 - val_accuracy: 0.7221 - val_loss: 0.1872\n",
      "Epoch 4/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7400 - loss: 0.1805 - val_accuracy: 0.7208 - val_loss: 0.1882\n",
      "Epoch 5/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7459 - loss: 0.1791 - val_accuracy: 0.7223 - val_loss: 0.1869\n",
      "Epoch 6/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7402 - loss: 0.1796 - val_accuracy: 0.7270 - val_loss: 0.1878\n",
      "Epoch 7/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7383 - loss: 0.1795 - val_accuracy: 0.7236 - val_loss: 0.1875\n",
      "Epoch 8/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7335 - loss: 0.1823 - val_accuracy: 0.7272 - val_loss: 0.1870\n",
      "Epoch 9/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7440 - loss: 0.1780 - val_accuracy: 0.7252 - val_loss: 0.1877\n",
      "Epoch 10/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7371 - loss: 0.1817 - val_accuracy: 0.7252 - val_loss: 0.1856\n",
      "Epoch 11/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7400 - loss: 0.1805 - val_accuracy: 0.7240 - val_loss: 0.1866\n",
      "Epoch 12/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7425 - loss: 0.1794 - val_accuracy: 0.7238 - val_loss: 0.1876\n",
      "Epoch 13/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7378 - loss: 0.1806 - val_accuracy: 0.7266 - val_loss: 0.1863\n",
      "Epoch 14/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7436 - loss: 0.1788 - val_accuracy: 0.7234 - val_loss: 0.1870\n",
      "Epoch 15/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7436 - loss: 0.1774 - val_accuracy: 0.7250 - val_loss: 0.1874\n",
      "Epoch 16/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7393 - loss: 0.1810 - val_accuracy: 0.7247 - val_loss: 0.1872\n",
      "Epoch 17/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7392 - loss: 0.1792 - val_accuracy: 0.7241 - val_loss: 0.1863\n",
      "Epoch 18/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7431 - loss: 0.1777 - val_accuracy: 0.7254 - val_loss: 0.1872\n",
      "Epoch 19/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7377 - loss: 0.1797 - val_accuracy: 0.7244 - val_loss: 0.1869\n",
      "Epoch 20/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7417 - loss: 0.1790 - val_accuracy: 0.7240 - val_loss: 0.1878\n",
      "Epoch 21/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7409 - loss: 0.1799 - val_accuracy: 0.7247 - val_loss: 0.1880\n",
      "Epoch 22/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7391 - loss: 0.1794 - val_accuracy: 0.7256 - val_loss: 0.1870\n",
      "Epoch 23/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7442 - loss: 0.1775 - val_accuracy: 0.7279 - val_loss: 0.1869\n",
      "Epoch 24/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7417 - loss: 0.1769 - val_accuracy: 0.7252 - val_loss: 0.1885\n",
      "Epoch 25/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7469 - loss: 0.1782 - val_accuracy: 0.7263 - val_loss: 0.1864\n",
      "Epoch 26/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7394 - loss: 0.1804 - val_accuracy: 0.7250 - val_loss: 0.1870\n",
      "Epoch 27/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7392 - loss: 0.1797 - val_accuracy: 0.7245 - val_loss: 0.1873\n",
      "Epoch 28/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7460 - loss: 0.1764 - val_accuracy: 0.7245 - val_loss: 0.1867\n",
      "Epoch 29/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7419 - loss: 0.1784 - val_accuracy: 0.7269 - val_loss: 0.1869\n",
      "Epoch 30/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7430 - loss: 0.1782 - val_accuracy: 0.7251 - val_loss: 0.1869\n",
      "Epoch 31/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7402 - loss: 0.1790 - val_accuracy: 0.7235 - val_loss: 0.1871\n",
      "Epoch 32/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7479 - loss: 0.1765 - val_accuracy: 0.7241 - val_loss: 0.1880\n",
      "Epoch 33/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7409 - loss: 0.1790 - val_accuracy: 0.7256 - val_loss: 0.1880\n",
      "Epoch 34/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7424 - loss: 0.1779 - val_accuracy: 0.7259 - val_loss: 0.1889\n",
      "Epoch 35/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 7ms/step - accuracy: 0.7415 - loss: 0.1777 - val_accuracy: 0.7252 - val_loss: 0.1872\n",
      "Epoch 36/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7442 - loss: 0.1777 - val_accuracy: 0.7254 - val_loss: 0.1874\n",
      "Epoch 37/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7441 - loss: 0.1775 - val_accuracy: 0.7214 - val_loss: 0.1890\n",
      "Epoch 38/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7398 - loss: 0.1787 - val_accuracy: 0.7238 - val_loss: 0.1874\n",
      "Epoch 39/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7429 - loss: 0.1788 - val_accuracy: 0.7262 - val_loss: 0.1901\n",
      "Epoch 40/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7401 - loss: 0.1787 - val_accuracy: 0.7256 - val_loss: 0.1875\n",
      "Epoch 41/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7416 - loss: 0.1784 - val_accuracy: 0.7231 - val_loss: 0.1885\n",
      "Epoch 42/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7452 - loss: 0.1778 - val_accuracy: 0.7247 - val_loss: 0.1872\n",
      "Epoch 43/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7454 - loss: 0.1768 - val_accuracy: 0.7257 - val_loss: 0.1877\n",
      "Epoch 44/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.7417 - loss: 0.1785 - val_accuracy: 0.7255 - val_loss: 0.1877\n",
      "Epoch 45/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7429 - loss: 0.1795 - val_accuracy: 0.7264 - val_loss: 0.1875\n",
      "Epoch 46/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7422 - loss: 0.1790 - val_accuracy: 0.7254 - val_loss: 0.1876\n",
      "Epoch 47/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7410 - loss: 0.1795 - val_accuracy: 0.7249 - val_loss: 0.1872\n",
      "Epoch 48/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7396 - loss: 0.1794 - val_accuracy: 0.7245 - val_loss: 0.1872\n",
      "Epoch 49/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 8ms/step - accuracy: 0.7395 - loss: 0.1791 - val_accuracy: 0.7240 - val_loss: 0.1884\n",
      "Epoch 50/50\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.7416 - loss: 0.1785 - val_accuracy: 0.7243 - val_loss: 0.1878\n"
     ]
    }
   ],
   "source": [
    "# Fit the model with the defined callback\n",
    "history = model.fit(X_train_scaled, \n",
    "                    y_train, \n",
    "                    epochs=50, \n",
    "                    batch_size=32, \n",
    "                    validation_data=(X_test_scaled, y_test),\n",
    "                    callbacks=[checkpoint_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependency\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a DataFrame containing training history\n",
    "charity_df = pd.DataFrame(history.history)\n",
    "\n",
    "# Increase the index by 1 to match the number of epochs\n",
    "charity_df.index += 1\n",
    "\n",
    "# Plot the loss\n",
    "charity_df.plot(y=\"loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accuracy\n",
    "charity_df.plot(y=\"accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m268/268\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7301 - loss: 0.1859\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.18777574598789215, 0.7243148684501648]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model using the preprocessed test data\n",
    "model.evaluate(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully to AlphabetSoupCharity.h5\n"
     ]
    }
   ],
   "source": [
    "# Export our model to HDF5 file\n",
    "# Define the file path for saving the model\n",
    "model_path = \"AlphabetSoupCharity.h5\"\n",
    "\n",
    "# Save the model to HDF5 file\n",
    "model.save(model_path)\n",
    "\n",
    "print(\"Model saved successfully to\", model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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