AlphabetSoupCharity_Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deliverable 1: Preprocessing the Data for a Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIN</th>\n",
       "      <th>NAME</th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10520599</td>\n",
       "      <td>BLUE KNIGHTS MOTORCYCLE CLUB</td>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10531628</td>\n",
       "      <td>AMERICAN CHESAPEAKE CLUB CHARITABLE TR</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10547893</td>\n",
       "      <td>ST CLOUD PROFESSIONAL FIREFIGHTERS</td>\n",
       "      <td>T5</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C3000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10553066</td>\n",
       "      <td>SOUTHSIDE ATHLETIC ASSOCIATION</td>\n",
       "      <td>T3</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>10000-24999</td>\n",
       "      <td>N</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10556103</td>\n",
       "      <td>GENETIC RESEARCH INSTITUTE OF THE DESERT</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EIN                                      NAME APPLICATION_TYPE  \\\n",
       "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
       "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
       "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
       "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
       "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
       "\n",
       "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
       "0       Independent          C1000    ProductDev   Association       1   \n",
       "1       Independent          C2000  Preservation  Co-operative       1   \n",
       "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
       "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
       "4       Independent          C1000     Heathcare         Trust       1   \n",
       "\n",
       "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
       "0              0                      N     5000              1  \n",
       "1         1-9999                      N   108590              1  \n",
       "2              0                      N     5000              0  \n",
       "3    10000-24999                      N     6692              1  \n",
       "4  100000-499999                      N   142590              1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "#  Import and read the charity_data.csv.\n",
    "import pandas as pd \n",
    "application_df = pd.read_csv(\"resource/charity_data.csv\")\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EIN', 'NAME', 'APPLICATION_TYPE', 'AFFILIATION', 'CLASSIFICATION',\n",
       "       'USE_CASE', 'ORGANIZATION', 'STATUS', 'INCOME_AMT',\n",
       "       'SPECIAL_CONSIDERATIONS', 'ASK_AMT', 'IS_SUCCESSFUL'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "application_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>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>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>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>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>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": [
       "                                       NAME APPLICATION_TYPE  \\\n",
       "0              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
       "1    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
       "2        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
       "3            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
       "4  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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
    "application_df.drop(['EIN'], axis=1, inplace=True)\n",
    "application_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME                      19568\n",
       "APPLICATION_TYPE             17\n",
       "AFFILIATION                   6\n",
       "CLASSIFICATION               71\n",
       "USE_CASE                      5\n",
       "ORGANIZATION                  4\n",
       "INCOME_AMT                    9\n",
       "SPECIAL_CONSIDERATIONS        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of unique values in each column.\n",
    "application_cat = application_df.dtypes[application_df.dtypes == \"object\"].index.tolist()\n",
    "\n",
    "application_df[application_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PARENT BOOSTER USA INC                                                  1260\n",
       "TOPS CLUB INC                                                            765\n",
       "UNITED STATES BOWLING CONGRESS INC                                       700\n",
       "WASHINGTON STATE UNIVERSITY                                              492\n",
       "AMATEUR ATHLETIC UNION OF THE UNITED STATES INC                          408\n",
       "                                                                        ... \n",
       "ST LOUIS SLAM WOMENS FOOTBALL                                              1\n",
       "AIESEC ALUMNI IBEROAMERICA CORP                                            1\n",
       "WEALLBLEEDRED ORG INC                                                      1\n",
       "AMERICAN SOCIETY FOR STANDARDS IN MEDIUMSHIP & PSYCHICAL INVESTIGATI       1\n",
       "WATERHOUSE CHARITABLE TR                                                   1\n",
       "Name: NAME, Length: 19568, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at NAME value counts for binning\n",
    "name_count = application_df['NAME'].value_counts()\n",
    "name_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 6,
     "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 NAME\n",
    "name_count.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PARENT BOOSTER USA INC                                    1260\n",
       "TOPS CLUB INC                                              765\n",
       "UNITED STATES BOWLING CONGRESS INC                         700\n",
       "WASHINGTON STATE UNIVERSITY                                492\n",
       "AMATEUR ATHLETIC UNION OF THE UNITED STATES INC            408\n",
       "                                                          ... \n",
       "ACTS MISSIONS                                                5\n",
       "KAPPA ALPHA PSI FRATERNITY                                   5\n",
       "SOCIETY OF MAYFLOWER DESCENDANTS IN THE STATE OF TEXAS       5\n",
       "INTERNATIONAL CONGRESS OF CHURCHES AND MINISTRIES            5\n",
       "PENN STATE ALUMNI ASSOCIATION                                5\n",
       "Name: NAME, Length: 402, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_count[name_count>4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMALGAMATED TRANSIT UNION                                               4\n",
       "4-H CLUBS & AFFILIATED 4-H ORGANIZATIONS                                4\n",
       "TEXAS WATER UTILITIES ASSOCIATION                                       4\n",
       "PARENTS FAMILIES AND FRIENDS OF LESBIANS AND GAYS INC                   4\n",
       "THE UNITED STATE PONY CLUBS INC                                         4\n",
       "                                                                       ..\n",
       "ST LOUIS SLAM WOMENS FOOTBALL                                           1\n",
       "AIESEC ALUMNI IBEROAMERICA CORP                                         1\n",
       "WEALLBLEEDRED ORG INC                                                   1\n",
       "AMERICAN SOCIETY FOR STANDARDS IN MEDIUMSHIP & PSYCHICAL INVESTIGATI    1\n",
       "WATERHOUSE CHARITABLE TR                                                1\n",
       "Name: NAME, Length: 19166, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_count[name_count<=4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                                                                19803\n",
       "PARENT BOOSTER USA INC                                                1260\n",
       "TOPS CLUB INC                                                          765\n",
       "UNITED STATES BOWLING CONGRESS INC                                     700\n",
       "WASHINGTON STATE UNIVERSITY                                            492\n",
       "                                                                     ...  \n",
       "GUILDS OF THE SANTA FE OPERA INC                                         5\n",
       "FAMILY MOTOR COACH ASSOCIATION                                           5\n",
       "JAMESTOWN SOCIETY                                                        5\n",
       "MOST WORSHIPFUL PRINCE HALL GRAND LODGE OF FREE & ACCEPTED MASONS        5\n",
       "PTA HAWAII CONGRESS                                                      5\n",
       "Name: NAME, Length: 403, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ...?\n",
    "replace_name = list(name_count[name_count <= 4].index)\n",
    "\n",
    "# Replace in dataframe\n",
    "for name in replace_name:\n",
    "    application_df.NAME = application_df.NAME.replace(name,\"Other\")\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "application_df.NAME.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3     27037\n",
       "T4      1542\n",
       "T6      1216\n",
       "T5      1173\n",
       "T19     1065\n",
       "T8       737\n",
       "T7       725\n",
       "T10      528\n",
       "T9       156\n",
       "T13       66\n",
       "T12       27\n",
       "T2        16\n",
       "T25        3\n",
       "T14        3\n",
       "T29        2\n",
       "T15        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at APPLICATION_TYPE value counts for binning\n",
    "app_type_count = application_df['APPLICATION_TYPE'].value_counts()\n",
    "app_type_count"
   ]
  },
  {
   "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 APPLICATION_TYPE\n",
    "app_type_count.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3       27037\n",
       "Other     2266\n",
       "T4        1542\n",
       "T6        1216\n",
       "T5        1173\n",
       "T19       1065\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ...?\n",
    "\n",
    "replace_application = list(app_type_count[app_type_count < 1000].index)\n",
    "#  YOUR CODE GOES HERE\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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "         ...  \n",
       "C4120        1\n",
       "C8210        1\n",
       "C2561        1\n",
       "C4500        1\n",
       "C2150        1\n",
       "Name: CLASSIFICATION, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "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": 15,
   "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": 15,
     "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NAME',\n",
       " 'APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vuvie/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 441 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NAME_AACE INTERNATIONAL  NAME_ACE MENTOR PROGRAM OF AMERICA INC  \\\n",
       "0                      0.0                                     0.0   \n",
       "1                      0.0                                     0.0   \n",
       "2                      0.0                                     0.0   \n",
       "3                      0.0                                     0.0   \n",
       "4                      0.0                                     0.0   \n",
       "\n",
       "   NAME_ACTS MINISTRY  NAME_ACTS MISSIONS  \\\n",
       "0                 0.0                 0.0   \n",
       "1                 0.0                 0.0   \n",
       "2                 0.0                 0.0   \n",
       "3                 0.0                 0.0   \n",
       "4                 0.0                 0.0   \n",
       "\n",
       "   NAME_AFRICAN-AMERICAN POSTAL LEAGUE UNITED FOR SUCCESS A-PLUS  \\\n",
       "0                                                0.0               \n",
       "1                                                0.0               \n",
       "2                                                0.0               \n",
       "3                                                0.0               \n",
       "4                                                0.0               \n",
       "\n",
       "   NAME_AIR FORCE ASSOCIATION  NAME_ALABAMA FEDERATION OF WOMENS CLUBS  \\\n",
       "0                         0.0                                      0.0   \n",
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       "2                         0.0                                      0.0   \n",
       "3                         0.0                                      0.0   \n",
       "4                         0.0                                      0.0   \n",
       "\n",
       "   NAME_ALABAMA TREASURE FOREST ASSOCIATION  \\\n",
       "0                                       0.0   \n",
       "1                                       0.0   \n",
       "2                                       0.0   \n",
       "3                                       0.0   \n",
       "4                                       0.0   \n",
       "\n",
       "   NAME_ALBANY STATE UNIVERSITY NATIONAL ALUMNI ASSOCIATION  \\\n",
       "0                                                0.0          \n",
       "1                                                0.0          \n",
       "2                                                0.0          \n",
       "3                                                0.0          \n",
       "4                                                0.0          \n",
       "\n",
       "   NAME_ALPHA PHI OMEGA  ...  INCOME_AMT_1-9999  INCOME_AMT_10000-24999  \\\n",
       "0                   0.0  ...                0.0                     0.0   \n",
       "1                   0.0  ...                1.0                     0.0   \n",
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       "3                   0.0  ...                0.0                     1.0   \n",
       "4                   0.0  ...                0.0                     0.0   \n",
       "\n",
       "   INCOME_AMT_100000-499999  INCOME_AMT_10M-50M  INCOME_AMT_1M-5M  \\\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                       1.0                 0.0               0.0   \n",
       "\n",
       "   INCOME_AMT_25000-99999  INCOME_AMT_50M+  INCOME_AMT_5M-10M  \\\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",
       "   SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y  \n",
       "0                       1.0                       0.0  \n",
       "1                       1.0                       0.0  \n",
       "2                       1.0                       0.0  \n",
       "3                       1.0                       0.0  \n",
       "4                       1.0                       0.0  \n",
       "\n",
       "[5 rows x 441 columns]"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vuvie/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>STATUS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "      <th>NAME_AACE INTERNATIONAL</th>\n",
       "      <th>NAME_ACE MENTOR PROGRAM OF AMERICA INC</th>\n",
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      "text/plain": [
       "   STATUS  ASK_AMT  IS_SUCCESSFUL  NAME_AACE INTERNATIONAL  \\\n",
       "0       1     5000              1                      0.0   \n",
       "1       1   108590              1                      0.0   \n",
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       "3       1     6692              1                      0.0   \n",
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       "\n",
       "   NAME_ACE MENTOR PROGRAM OF AMERICA INC  NAME_ACTS MINISTRY  \\\n",
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       "\n",
       "   NAME_ACTS MISSIONS  \\\n",
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       "1                 0.0   \n",
       "2                 0.0   \n",
       "3                 0.0   \n",
       "4                 0.0   \n",
       "\n",
       "   NAME_AFRICAN-AMERICAN POSTAL LEAGUE UNITED FOR SUCCESS A-PLUS  \\\n",
       "0                                                0.0               \n",
       "1                                                0.0               \n",
       "2                                                0.0               \n",
       "3                                                0.0               \n",
       "4                                                0.0               \n",
       "\n",
       "   NAME_AIR FORCE ASSOCIATION  NAME_ALABAMA FEDERATION OF WOMENS CLUBS  ...  \\\n",
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       "3                         0.0                                      0.0  ...   \n",
       "4                         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",
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       "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",
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       "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",
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       "\n",
       "[5 rows x 444 columns]"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/vuvie/opt/anaconda3/envs/mlenv/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = application_df[\"IS_SUCCESSFUL\"].values\n",
    "X = application_df.drop([\"IS_SUCCESSFUL\"],1).values\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 80)                35520     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 30)                2430      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 31        \n",
      "=================================================================\n",
      "Total params: 37,981\n",
      "Trainable params: 37,981\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-20 02:40:24.964238: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\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",
    "#hidden_nodes_layer3 = 10\n",
    "\n",
    "nn = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "#nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"sigmoid\"))\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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-20 02:40:25.566304: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.5377 - accuracy: 0.7513\n",
      "\n",
      "Epoch 00001: saving model to checkpoints/weights.01.hdf5\n",
      "Epoch 2/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4290 - accuracy: 0.7992\n",
      "\n",
      "Epoch 00002: saving model to checkpoints/weights.02.hdf5\n",
      "Epoch 3/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4279 - accuracy: 0.7970\n",
      "\n",
      "Epoch 00003: saving model to checkpoints/weights.03.hdf5\n",
      "Epoch 4/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4243 - accuracy: 0.7970\n",
      "\n",
      "Epoch 00004: saving model to checkpoints/weights.04.hdf5\n",
      "Epoch 5/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4224 - accuracy: 0.7981\n",
      "\n",
      "Epoch 00005: saving model to checkpoints/weights.05.hdf5\n",
      "Epoch 6/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4189 - accuracy: 0.8014\n",
      "\n",
      "Epoch 00006: saving model to checkpoints/weights.06.hdf5\n",
      "Epoch 7/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4225 - accuracy: 0.7956\n",
      "\n",
      "Epoch 00007: saving model to checkpoints/weights.07.hdf5\n",
      "Epoch 8/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4168 - accuracy: 0.8032\n",
      "\n",
      "Epoch 00008: saving model to checkpoints/weights.08.hdf5\n",
      "Epoch 9/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4113 - accuracy: 0.8035: 0s - loss: 0.4112 - accuracy: 0.80\n",
      "\n",
      "Epoch 00009: saving model to checkpoints/weights.09.hdf5\n",
      "Epoch 10/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4110 - accuracy: 0.8071: 1s - loss: 0.398\n",
      "\n",
      "Epoch 00010: saving model to checkpoints/weights.10.hdf5\n",
      "Epoch 11/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4111 - accuracy: 0.8032\n",
      "\n",
      "Epoch 00011: saving model to checkpoints/weights.11.hdf5\n",
      "Epoch 12/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4105 - accuracy: 0.8039\n",
      "\n",
      "Epoch 00012: saving model to checkpoints/weights.12.hdf5\n",
      "Epoch 13/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4104 - accuracy: 0.8043: 0s - loss: 0.4099 - accuracy: 0. - ETA: 0s - loss: 0.4102 - accuracy: 0.80\n",
      "\n",
      "Epoch 00013: saving model to checkpoints/weights.13.hdf5\n",
      "Epoch 14/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.4086 - accuracy: 0.8055: 0s - loss: 0.4078 - accura\n",
      "\n",
      "Epoch 00014: saving model to checkpoints/weights.14.hdf5\n",
      "Epoch 15/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4118 - accuracy: 0.8029\n",
      "\n",
      "Epoch 00015: saving model to checkpoints/weights.15.hdf5\n",
      "Epoch 16/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4142 - accuracy: 0.8031\n",
      "\n",
      "Epoch 00016: saving model to checkpoints/weights.16.hdf5\n",
      "Epoch 17/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4019 - accuracy: 0.8111\n",
      "\n",
      "Epoch 00017: saving model to checkpoints/weights.17.hdf5\n",
      "Epoch 18/100\n",
      "804/804 [==============================] - 3s 4ms/step - loss: 0.4172 - accuracy: 0.8007\n",
      "\n",
      "Epoch 00018: saving model to checkpoints/weights.18.hdf5\n",
      "Epoch 19/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4071 - accuracy: 0.8090\n",
      "\n",
      "Epoch 00019: saving model to checkpoints/weights.19.hdf5\n",
      "Epoch 20/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4122 - accuracy: 0.8021: 0s - loss: 0.4121 - accu\n",
      "\n",
      "Epoch 00020: saving model to checkpoints/weights.20.hdf5\n",
      "Epoch 21/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4095 - accuracy: 0.8058\n",
      "\n",
      "Epoch 00021: saving model to checkpoints/weights.21.hdf5\n",
      "Epoch 22/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4089 - accuracy: 0.8032\n",
      "\n",
      "Epoch 00022: saving model to checkpoints/weights.22.hdf5\n",
      "Epoch 23/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4101 - accuracy: 0.8040\n",
      "\n",
      "Epoch 00023: saving model to checkpoints/weights.23.hdf5\n",
      "Epoch 24/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4053 - accuracy: 0.8061\n",
      "\n",
      "Epoch 00024: saving model to checkpoints/weights.24.hdf5\n",
      "Epoch 25/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4074 - accuracy: 0.8072\n",
      "\n",
      "Epoch 00025: saving model to checkpoints/weights.25.hdf5\n",
      "Epoch 26/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4116 - accuracy: 0.8064\n",
      "\n",
      "Epoch 00026: saving model to checkpoints/weights.26.hdf5\n",
      "Epoch 27/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4096 - accuracy: 0.8026\n",
      "\n",
      "Epoch 00027: saving model to checkpoints/weights.27.hdf5\n",
      "Epoch 28/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4097 - accuracy: 0.8036\n",
      "\n",
      "Epoch 00028: saving model to checkpoints/weights.28.hdf5\n",
      "Epoch 29/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4063 - accuracy: 0.8083\n",
      "\n",
      "Epoch 00029: saving model to checkpoints/weights.29.hdf5\n",
      "Epoch 30/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4063 - accuracy: 0.8071\n",
      "\n",
      "Epoch 00030: saving model to checkpoints/weights.30.hdf5\n",
      "Epoch 31/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4052 - accuracy: 0.8089\n",
      "\n",
      "Epoch 00031: saving model to checkpoints/weights.31.hdf5\n",
      "Epoch 32/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4045 - accuracy: 0.8093\n",
      "\n",
      "Epoch 00032: saving model to checkpoints/weights.32.hdf5\n",
      "Epoch 33/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4053 - accuracy: 0.8066\n",
      "\n",
      "Epoch 00033: saving model to checkpoints/weights.33.hdf5\n",
      "Epoch 34/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4020 - accuracy: 0.8082\n",
      "\n",
      "Epoch 00034: saving model to checkpoints/weights.34.hdf5\n",
      "Epoch 35/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4055 - accuracy: 0.8088\n",
      "\n",
      "Epoch 00035: saving model to checkpoints/weights.35.hdf5\n",
      "Epoch 36/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4028 - accuracy: 0.8109\n",
      "\n",
      "Epoch 00036: saving model to checkpoints/weights.36.hdf5\n",
      "Epoch 37/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4029 - accuracy: 0.8126\n",
      "\n",
      "Epoch 00037: saving model to checkpoints/weights.37.hdf5\n",
      "Epoch 38/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4105 - accuracy: 0.8048\n",
      "\n",
      "Epoch 00038: saving model to checkpoints/weights.38.hdf5\n",
      "Epoch 39/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4107 - accuracy: 0.8042: 0s - loss: 0.410\n",
      "\n",
      "Epoch 00039: saving model to checkpoints/weights.39.hdf5\n",
      "Epoch 40/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4026 - accuracy: 0.8099: 0s - loss: 0.4\n",
      "\n",
      "Epoch 00040: saving model to checkpoints/weights.40.hdf5\n",
      "Epoch 41/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4084 - accuracy: 0.8050: 1s - loss: 0.4095 - accuracy:  - E\n",
      "\n",
      "Epoch 00041: saving model to checkpoints/weights.41.hdf5\n",
      "Epoch 42/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4032 - accuracy: 0.8100: 0s - los\n",
      "\n",
      "Epoch 00042: saving model to checkpoints/weights.42.hdf5\n",
      "Epoch 43/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4020 - accuracy: 0.8111\n",
      "\n",
      "Epoch 00043: saving model to checkpoints/weights.43.hdf5\n",
      "Epoch 44/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4045 - accuracy: 0.8100\n",
      "\n",
      "Epoch 00044: saving model to checkpoints/weights.44.hdf5\n",
      "Epoch 45/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4021 - accuracy: 0.8098\n",
      "\n",
      "Epoch 00045: saving model to checkpoints/weights.45.hdf5\n",
      "Epoch 46/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4055 - accuracy: 0.8102\n",
      "\n",
      "Epoch 00046: saving model to checkpoints/weights.46.hdf5\n",
      "Epoch 47/100\n",
      "804/804 [==============================] - 3s 4ms/step - loss: 0.4034 - accuracy: 0.8090\n",
      "\n",
      "Epoch 00047: saving model to checkpoints/weights.47.hdf5\n",
      "Epoch 48/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4020 - accuracy: 0.8109\n",
      "\n",
      "Epoch 00048: saving model to checkpoints/weights.48.hdf5\n",
      "Epoch 49/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4003 - accuracy: 0.8106\n",
      "\n",
      "Epoch 00049: saving model to checkpoints/weights.49.hdf5\n",
      "Epoch 50/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4029 - accuracy: 0.8083\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00050: saving model to checkpoints/weights.50.hdf5\n",
      "Epoch 51/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3991 - accuracy: 0.8123\n",
      "\n",
      "Epoch 00051: saving model to checkpoints/weights.51.hdf5\n",
      "Epoch 52/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.3979 - accuracy: 0.8110\n",
      "\n",
      "Epoch 00052: saving model to checkpoints/weights.52.hdf5\n",
      "Epoch 53/100\n",
      "804/804 [==============================] - 3s 4ms/step - loss: 0.4036 - accuracy: 0.8091\n",
      "\n",
      "Epoch 00053: saving model to checkpoints/weights.53.hdf5\n",
      "Epoch 54/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4020 - accuracy: 0.8085\n",
      "\n",
      "Epoch 00054: saving model to checkpoints/weights.54.hdf5\n",
      "Epoch 55/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4005 - accuracy: 0.8117\n",
      "\n",
      "Epoch 00055: saving model to checkpoints/weights.55.hdf5\n",
      "Epoch 56/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4000 - accuracy: 0.8118\n",
      "\n",
      "Epoch 00056: saving model to checkpoints/weights.56.hdf5\n",
      "Epoch 57/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4052 - accuracy: 0.8084: 0s - loss: 0.4057 - accuracy: 0.80 - ETA: 0s - loss: 0.4057 - accuracy:  - ETA: 0s - loss: 0.4054 - accura\n",
      "\n",
      "Epoch 00057: saving model to checkpoints/weights.57.hdf5\n",
      "Epoch 58/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4037 - accuracy: 0.8073\n",
      "\n",
      "Epoch 00058: saving model to checkpoints/weights.58.hdf5\n",
      "Epoch 59/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4007 - accuracy: 0.8096: 0s - loss: 0.4005 - accura\n",
      "\n",
      "Epoch 00059: saving model to checkpoints/weights.59.hdf5\n",
      "Epoch 60/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4032 - accuracy: 0.8100\n",
      "\n",
      "Epoch 00060: saving model to checkpoints/weights.60.hdf5\n",
      "Epoch 61/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4026 - accuracy: 0.8104\n",
      "\n",
      "Epoch 00061: saving model to checkpoints/weights.61.hdf5\n",
      "Epoch 62/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3980 - accuracy: 0.8126\n",
      "\n",
      "Epoch 00062: saving model to checkpoints/weights.62.hdf5\n",
      "Epoch 63/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4019 - accuracy: 0.8094\n",
      "\n",
      "Epoch 00063: saving model to checkpoints/weights.63.hdf5\n",
      "Epoch 64/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4013 - accuracy: 0.8078\n",
      "\n",
      "Epoch 00064: saving model to checkpoints/weights.64.hdf5\n",
      "Epoch 65/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3974 - accuracy: 0.8129\n",
      "\n",
      "Epoch 00065: saving model to checkpoints/weights.65.hdf5\n",
      "Epoch 66/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4059 - accuracy: 0.8066\n",
      "\n",
      "Epoch 00066: saving model to checkpoints/weights.66.hdf5\n",
      "Epoch 67/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.3956 - accuracy: 0.8149\n",
      "\n",
      "Epoch 00067: saving model to checkpoints/weights.67.hdf5\n",
      "Epoch 68/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4012 - accuracy: 0.8077\n",
      "\n",
      "Epoch 00068: saving model to checkpoints/weights.68.hdf5\n",
      "Epoch 69/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4029 - accuracy: 0.8089\n",
      "\n",
      "Epoch 00069: saving model to checkpoints/weights.69.hdf5\n",
      "Epoch 70/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.4104 - accuracy: 0.8045\n",
      "\n",
      "Epoch 00070: saving model to checkpoints/weights.70.hdf5\n",
      "Epoch 71/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4059 - accuracy: 0.8067\n",
      "\n",
      "Epoch 00071: saving model to checkpoints/weights.71.hdf5\n",
      "Epoch 72/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3965 - accuracy: 0.8145\n",
      "\n",
      "Epoch 00072: saving model to checkpoints/weights.72.hdf5\n",
      "Epoch 73/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3996 - accuracy: 0.8101: 0s - loss: 0.3990 \n",
      "\n",
      "Epoch 00073: saving model to checkpoints/weights.73.hdf5\n",
      "Epoch 74/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3983 - accuracy: 0.8107\n",
      "\n",
      "Epoch 00074: saving model to checkpoints/weights.74.hdf5\n",
      "Epoch 75/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3958 - accuracy: 0.8129\n",
      "\n",
      "Epoch 00075: saving model to checkpoints/weights.75.hdf5\n",
      "Epoch 76/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3969 - accuracy: 0.8107\n",
      "\n",
      "Epoch 00076: saving model to checkpoints/weights.76.hdf5\n",
      "Epoch 77/100\n",
      "804/804 [==============================] - 3s 4ms/step - loss: 0.4011 - accuracy: 0.8087\n",
      "\n",
      "Epoch 00077: saving model to checkpoints/weights.77.hdf5\n",
      "Epoch 78/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3984 - accuracy: 0.8133\n",
      "\n",
      "Epoch 00078: saving model to checkpoints/weights.78.hdf5\n",
      "Epoch 79/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4011 - accuracy: 0.8091\n",
      "\n",
      "Epoch 00079: saving model to checkpoints/weights.79.hdf5\n",
      "Epoch 80/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3871 - accuracy: 0.8194: \n",
      "\n",
      "Epoch 00080: saving model to checkpoints/weights.80.hdf5\n",
      "Epoch 81/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4005 - accuracy: 0.8099\n",
      "\n",
      "Epoch 00081: saving model to checkpoints/weights.81.hdf5\n",
      "Epoch 82/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3984 - accuracy: 0.8119\n",
      "\n",
      "Epoch 00082: saving model to checkpoints/weights.82.hdf5\n",
      "Epoch 83/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3968 - accuracy: 0.8131\n",
      "\n",
      "Epoch 00083: saving model to checkpoints/weights.83.hdf5\n",
      "Epoch 84/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3977 - accuracy: 0.8129\n",
      "\n",
      "Epoch 00084: saving model to checkpoints/weights.84.hdf5\n",
      "Epoch 85/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3960 - accuracy: 0.8133\n",
      "\n",
      "Epoch 00085: saving model to checkpoints/weights.85.hdf5\n",
      "Epoch 86/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3987 - accuracy: 0.8115\n",
      "\n",
      "Epoch 00086: saving model to checkpoints/weights.86.hdf5\n",
      "Epoch 87/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3967 - accuracy: 0.8099\n",
      "\n",
      "Epoch 00087: saving model to checkpoints/weights.87.hdf5\n",
      "Epoch 88/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4034 - accuracy: 0.8071\n",
      "\n",
      "Epoch 00088: saving model to checkpoints/weights.88.hdf5\n",
      "Epoch 89/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3973 - accuracy: 0.8123: 0s - los\n",
      "\n",
      "Epoch 00089: saving model to checkpoints/weights.89.hdf5\n",
      "Epoch 90/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3991 - accuracy: 0.8100: 0s - loss: 0.3991 - accuracy: 0.\n",
      "\n",
      "Epoch 00090: saving model to checkpoints/weights.90.hdf5\n",
      "Epoch 91/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4012 - accuracy: 0.8098\n",
      "\n",
      "Epoch 00091: saving model to checkpoints/weights.91.hdf5\n",
      "Epoch 92/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3998 - accuracy: 0.8109\n",
      "\n",
      "Epoch 00092: saving model to checkpoints/weights.92.hdf5\n",
      "Epoch 93/100\n",
      "804/804 [==============================] - 3s 3ms/step - loss: 0.3995 - accuracy: 0.8077\n",
      "\n",
      "Epoch 00093: saving model to checkpoints/weights.93.hdf5\n",
      "Epoch 94/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3981 - accuracy: 0.8079\n",
      "\n",
      "Epoch 00094: saving model to checkpoints/weights.94.hdf5\n",
      "Epoch 95/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3947 - accuracy: 0.8115\n",
      "\n",
      "Epoch 00095: saving model to checkpoints/weights.95.hdf5\n",
      "Epoch 96/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3986 - accuracy: 0.8114\n",
      "\n",
      "Epoch 00096: saving model to checkpoints/weights.96.hdf5\n",
      "Epoch 97/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.3977 - accuracy: 0.8100\n",
      "\n",
      "Epoch 00097: saving model to checkpoints/weights.97.hdf5\n",
      "Epoch 98/100\n",
      "804/804 [==============================] - 2s 2ms/step - loss: 0.3974 - accuracy: 0.8123: 0s - loss: 0.3970 - accuracy\n",
      "\n",
      "Epoch 00098: saving model to checkpoints/weights.98.hdf5\n",
      "Epoch 99/100\n",
      "804/804 [==============================] - 2s 3ms/step - loss: 0.4003 - accuracy: 0.8120\n",
      "\n",
      "Epoch 00099: saving model to checkpoints/weights.99.hdf5\n",
      "Epoch 100/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "804/804 [==============================] - 3s 3ms/step - loss: 0.3981 - accuracy: 0.8083\n",
      "\n",
      "Epoch 00100: saving model to checkpoints/weights.100.hdf5\n"
     ]
    }
   ],
   "source": [
    "# Create a callback that saves the model's weights every epoch\n",
    "cp_callback = ModelCheckpoint(\n",
    "    filepath=checkpoint_path,\n",
    "    verbose=1,\n",
    "    save_weights_only=True,\n",
    "    save_freq='epoch')\n",
    "\n",
    "# Train the model\n",
    "fit_model = nn.fit(X_train_scaled,y_train,epochs=100,callbacks=[cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 1s - loss: 0.4557 - accuracy: 0.7915\n",
      "Loss: 0.4556748569011688, Accuracy: 0.7914868593215942\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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export model to HDF5 file\n",
    "nn.save('AlphabetSoupCharity_Optimization.h5')"
   ]
  }
 ],
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    "name": "ipython",
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