AlphabetSoupCharity_Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIN</th>\n",
       "      <th>NAME</th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10520599</td>\n",
       "      <td>BLUE KNIGHTS MOTORCYCLE CLUB</td>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10531628</td>\n",
       "      <td>AMERICAN CHESAPEAKE CLUB CHARITABLE TR</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10547893</td>\n",
       "      <td>ST CLOUD PROFESSIONAL FIREFIGHTERS</td>\n",
       "      <td>T5</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C3000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10553066</td>\n",
       "      <td>SOUTHSIDE ATHLETIC ASSOCIATION</td>\n",
       "      <td>T3</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>10000-24999</td>\n",
       "      <td>N</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10556103</td>\n",
       "      <td>GENETIC RESEARCH INSTITUTE OF THE DESERT</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EIN                                      NAME APPLICATION_TYPE  \\\n",
       "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
       "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
       "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
       "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
       "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
       "\n",
       "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
       "0       Independent          C1000    ProductDev   Association       1   \n",
       "1       Independent          C2000  Preservation  Co-operative       1   \n",
       "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
       "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
       "4       Independent          C1000     Heathcare         Trust       1   \n",
       "\n",
       "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
       "0              0                      N     5000              1  \n",
       "1         1-9999                      N   108590              1  \n",
       "2              0                      N     5000              0  \n",
       "3    10000-24999                      N     6692              1  \n",
       "4  100000-499999                      N   142590              1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "#  Import and read the charity_data.csv.\n",
    "import pandas as pd \n",
    "df = pd.read_csv(\"../Resources/charity_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T5</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C3000</td>\n",
       "      <td>ProductDev</td>\n",
       "      <td>Association</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T3</td>\n",
       "      <td>CompanySponsored</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>10000-24999</td>\n",
       "      <td>N</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  APPLICATION_TYPE       AFFILIATION CLASSIFICATION      USE_CASE  \\\n",
       "0              T10       Independent          C1000    ProductDev   \n",
       "1               T3       Independent          C2000  Preservation   \n",
       "2               T5  CompanySponsored          C3000    ProductDev   \n",
       "3               T3  CompanySponsored          C2000  Preservation   \n",
       "4               T3       Independent          C1000     Heathcare   \n",
       "\n",
       "   ORGANIZATION  STATUS     INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  \\\n",
       "0   Association       1              0                      N     5000   \n",
       "1  Co-operative       1         1-9999                      N   108590   \n",
       "2   Association       1              0                      N     5000   \n",
       "3         Trust       1    10000-24999                      N     6692   \n",
       "4         Trust       1  100000-499999                      N   142590   \n",
       "\n",
       "   IS_SUCCESSFUL  \n",
       "0              1  \n",
       "1              1  \n",
       "2              0  \n",
       "3              1  \n",
       "4              1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the non-beneficial ID columns, 'EIN' and 'NAME'.\n",
    "df = df.drop(['EIN', 'NAME'],1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE            17\n",
       "AFFILIATION                  6\n",
       "CLASSIFICATION              71\n",
       "USE_CASE                     5\n",
       "ORGANIZATION                 4\n",
       "STATUS                       2\n",
       "INCOME_AMT                   9\n",
       "SPECIAL_CONSIDERATIONS       2\n",
       "ASK_AMT                   8747\n",
       "IS_SUCCESSFUL                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the number of unique values in each column.\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3     27037\n",
       "T4      1542\n",
       "T6      1216\n",
       "T5      1173\n",
       "T19     1065\n",
       "T8       737\n",
       "T7       725\n",
       "T10      528\n",
       "T9       156\n",
       "T13       66\n",
       "T12       27\n",
       "T2        16\n",
       "T14        3\n",
       "T25        3\n",
       "T29        2\n",
       "T15        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at APPLICATION_TYPE value counts for binning\n",
    "APPL_column_Series = df['APPLICATION_TYPE'].value_counts()\n",
    "APPL_column_Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299bfae77c8>"
      ]
     },
     "execution_count": 5,
     "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",
    "APPL_column_Series.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3        27037\n",
       "T4         1542\n",
       "T6         1216\n",
       "T5         1173\n",
       "T19        1065\n",
       "T8          737\n",
       "T7          725\n",
       "T10         528\n",
       "Others      276\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ...?\n",
    "replace_APPL_list = APPL_column_Series[APPL_column_Series < 500].index.tolist()\n",
    "\n",
    "# Replace in dataframe\n",
    "for app in replace_APPL_list:\n",
    "    df.APPLICATION_TYPE = df.APPLICATION_TYPE.replace(app,'Others')\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "df.APPLICATION_TYPE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "         ...  \n",
       "C2190        1\n",
       "C2570        1\n",
       "C3700        1\n",
       "C8210        1\n",
       "C2170        1\n",
       "Name: CLASSIFICATION, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at CLASSIFICATION value counts for binning\n",
    "CLASS_column_Series = df.CLASSIFICATION.value_counts()\n",
    "CLASS_column_Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299bfcdea08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iZhuAvwZ2uvvbiXR3PxWWg8A3iXfTSR4sdMbnhESLSFPWiBS3KIPNQaDVzDabWTlwJ7BvVp59wM4wKm0r0B+6xtKV3Ud8AABh+QyAmTUAfwN80d3/IXECMys1s+awXgZ8Djic+7crqfSNjFNVFqOyLLag8upGE1ke5jfn+zy4+6SZPQDsB2LAE+5+xMzuC/sfA54FbgPagRHgnnRlw6EfAp42s3uBE8AdIf0B4CrgQTN7MKR9BhgG9odAEwOeB74W1fuW90vM+LxQibLqRhMpbpEFGwB3f5Z4QElOeyxp3YH7sy0b0s8Dt6ZI/wPgD+aoyk3Z11pyqW9kYTM+J1SUxqgpj6kbTaTIaQYBiVTvIuZFS2is0fxoIsVOwUYi1TcyPnPdZaEaq8v1tE6RIqdgI5HqG5lgxSJbNg3VZepGEylyCjYSGXen78Liu9Gaaso1QECkyCnYSGQGxyaZmvacdKNp6LNIcVOwkcj0h66vFVWL70YbHJ1kYmo6F9USkQLIKtiY2bfN7B+bmYKTZC3RGllsy6YpPHhNk3GKFK9sg8ejwD8HjpnZQ2Z2TYR1kmVisVPVJCTu09HwZ5HilVWwcffn3f1u4EbgXeA5M/vfZnZPuDNf5ANmJuFcbMsmlO/RIAGRopV1t5iZrQR+Dfh14MfAV4gHn+ciqZkUvf4LuWrZaDJOkWKX1XQ1ZvZXwDXEn375TxLPkwG+ZWaHoqqcFLfEUzobFjlAoLFG3WgixS7budH+NMxVNsPMKtx9zN3bIqiXLAN9F8apqyilNLa4cSUz3WgKNiJFK9tfgVQTXP4glxWR5advZIKGBT6hM1lVeYyK0hKNRhMpYmlbNma2hvjjmKvM7CNcfIJmPVAdcd2kyPWNjC/4CZ2zNVZrFgGRYpapG+2zxAcFbAD+S1L6IPB7EdVJlonFPssmWWONZhEQKWZpg4277wX2mtkvuvu381QnWSb6L0ywsSk3DeBGTcYpUtTSXrMxs18Jq5vM7F/PfmU6uJltM7OjZtZuZntS7Dczezjsf83MbsxU1syazOw5MzsWlo0h/dNm9rKZvR6WP5dU5qaQ3h7OZ0jkekfGFz0JZ0KjJuMUKWqZBgjUhGUtUJfiNScziwGPANuBLcBdZrZlVrbtQGt47SY+U0GmsnuAA+7eChwI2wDdxIdlXw/sIj5MO+HRcPzEubZleN+ySNPTTv+FiUUPe06It2wUbESKVaZutK+G5e8v4Ng3A+3ufhzAzJ4CdgBvJOXZATwZHg/9opk1mNlaYFOasjuAT4Xye4EXgC+4+4+TjnsEqDSzCqAJqHf3H4RjPQncDnx3Ae9JsjQwOoH74mcPSGisLqf/wgRT006sRA1TkWKT7UScXzazejMrM7MDZtad1MU2l/XAyaTtjpCWTZ50ZVcnbioNy1Upzv2LwI/dfSyU68hQD8mxXM2LltBYXc60w8AFXbcRKUbZ3mfzGXcfAD5H/Mf6Q8C/yVAm1X8/Pcs82ZRNfVKza4E/BH5zHvVIlN1tZofM7FBXV1c2p5M55GrG54TGmsSUNepKEylG2QabxH9PbwP+3N17sijTAWxM2t4AnM4yT7qyZ0NXG2F5LpHJzDYAfw3sdPe3k86xIUM9AHD3x929zd3bWlpaMr5BmVtfaIEs9pHQCYnuOI1IEylO2Qab/2FmbwFtwAEzawFGM5Q5CLSa2WYzKwfuBPbNyrMP2BlGpW0F+kPXWLqy+4gPACAsnwEwswbgb4Avuvs/JE4QjjdoZlvDKLSdiTISnb4ct2wSU9ZoRJpIccr2EQN7gI8Bbe4+AQwTv1Cfrswk8ACwH3gTeNrdj5jZfWZ2X8j2LHAcaAe+BvxWurKhzEPAp83sGPDpsE3IfxXwoJm9El6J6zmfB/40nOdtNDggcjPXbHI2Gi3RslGwESlG2U7ECfBh4vfbJJd5Ml2BMHnns7PSHktad+D+bMuG9PPArSnS/4DUc7jh7oeA69LVVXKrd2QCM6jPVbDRNRuRopbtIwa+AVwJvAJMhWQnQ7CRS1f/yDj1lWU5G6ZcW1FKaYnpmo1Ikcq2ZdMGbAktEZGM+i5M5Gz2AAAzo6G6XM+0ESlS2Q4QOAysibIisrz0jkywIkeDAxKaasr0aGiRIpVty6YZeMPMXgLGEonu/k8jqZUUvf6R8ZknbOZKQ3W5utFEilS2weZLUVZClp/ekQk2N9dkzjgPjdVlvNM9nNNjikh+ZDv0+X8B7wJlYf0g8KMI6yVFrm9kPGfzoiU01ZTTM6yWjUgxynZutN8A/hL4akhaD3wnqkpJcZucmmZgdDJn86IlJAYIaJyKSPHJdoDA/cDHgQEAdz9G6gkwRRgYnQRyN3tAQlN1OZPTzuDYZE6PKyLRyzbYjLn7zDCgcGOn/nspKSVuvMx9yyZ+vD51pYkUnWyDzf8ys98Dqszs08BfAP8jumpJMbv4eIHctmw0ZY1I8co22OwBuoDXiU/d/yzw76KqlBS3xI2XuZoXLSExlLpHwUak6GQ19Nndp83sO8B33F0PepG0Ejde5vqaTWJGAs0iIFJ80rZswtT/XzKzbuAt4KiZdZnZ/5Wf6kkxSnRzNdVG1I2mazYiRSdTN9q/JD4K7aPuvtLdm4BbgI+b2b+KvHZSlM4Pj1NeWkJNeSynx62vKqPEdM1GpBhlCjY7gbvc/Z1EgrsfB34l7BP5gN7hcZqqy4k/qy53YiXGiqoyBRuRIpQp2JS5e/fsxHDdJrdXf2XZ6BkepynH86IlNNaUqxtNpAhlCjbp/gup/15KSlEGm6bqcs38LFKEMgWbnzazgRSvQeD6TAc3s21mdtTM2s1sT4r9ZmYPh/2vmdmNmcqaWZOZPWdmx8KyMaSvNLPvmdmQmf3JrPO8EI41+3HREoEog83K2nK6h8YyZxSRJSVtsHH3mLvXp3jVuXvabjQziwGPANuBLcBdZrZlVrbtQGt47QYezaLsHuCAu7cCB8I2wCjwIPC7c1Tpbne/IbzOpau7LE6Uwaa5tkLBRqQIZXtT50LcDLS7+/Ew1c1TwI5ZeXYAT3rci0CDma3NUHYHsDes7wVuB3D3YXf/PvGgIwUyESbhjDLY9I5MMDk1HcnxRSQaUQab9cDJpO2OkJZNnnRlV7t7J0BYZtsl9mehC+1Bm2OYlJntNrNDZnaoq0v3ri5Eb7ieElmwqasA0HUbkSITZbBJ9YM+e/LOufJkU3Y+7nb364FPhtevpsrk7o+7e5u7t7W0tCzidJeuxFQykQWbcNwudaWJFJUog00HsDFpewNwOss86cqeDV1thGXG6y/ufiosB4FvEu+mkwj0DOWnZdM9pJaNSDGJMtgcBFrNbLOZlQN3Avtm5dkH7Ayj0rYC/aFrLF3ZfcCusL4LeCZdJcys1Myaw3oZ8Dng8OLfnqQSecumNgSbQbVsRIpJVhNxLoS7T5rZA8B+IAY84e5HzOy+sP8x4rNH3wa0AyPAPenKhkM/BDxtZvcCJ4A7Euc0s3eBeqDczG4HPgO8B+wPgSYGPA98Lar3fanrifqaTZhv7fywgo1IMYks2AC4+7PEA0py2mNJ6078KaBZlQ3p54Fb5yizaY6q3JRdjWWxEsEm148XSKitKKW8tETdaCJFJspuNLkE9QyP01BdRmksmq+WmdFSW6FuNJEio2AjOXU+TMIZpebaco1GEykyCjaSU70Rzh6QEJ9FQN1oIsVEwUZyKsqpahJW1pZzXi0bkaKiYCM51T00zsocP6FztubaCs4PjzM9vZj7fEUknxRsJGempp2e4TFawr0wUWmurWBq2um7oOfaiBQLBRvJmfPDY0w7tNRFHGxmZhFQV5pIsVCwkZzpHoxftI882IRuui4NfxYpGgo2kjOJ4cjNEXejra6vBODsgJ4mIVIsFGwkZxItjahbNmtCsDmjYCNSNBRsJGe689Syqakopa6ilHMD6kYTKRYKNpIzXYNjVJfHqKmIdMo9AFavqORMv1o2IsVCwUZypmtwLPIutIQ19ZXqRhMpIgo2kjPdQ9HfY5Owqr6Ccwo2IkVDwUZypmtwLPLrNQlr6is5NzimWQREioSCjeRM11Aeu9FWVDI57XTrIWoiRSHSYGNm28zsqJm1m9meFPvNzB4O+18zsxszlTWzJjN7zsyOhWVjSF9pZt8zsyEz+5NZ57nJzF4Px3rYzCzK930pGp+cpm9kIm/BZuZem34FG5FiEFmwMbMY8AiwHdgC3GVmW2Zl2w60htdu4NEsyu4BDrh7K3AgbAOMAg8Cv5uiOo+G4yfOtS0Hb1GSJB7TnM9uNNCNnSLFIsqWzc1Au7sfd/dx4Clgx6w8O4AnPe5FoMHM1mYouwPYG9b3ArcDuPuwu3+feNCZEY5X7+4/CI+hfjJRRnInXzd0JqzWjZ0iRSXKYLMeOJm03RHSssmTruxqd+8ECMtVWdSjI0M9ZJHyHWyaa8spMbVsRIpFlMEm1XWR2UOH5sqTTdlc1iOe0Wy3mR0ys0NdXV0LPN2lKd/BpjRWQktdhYKNSJGIMth0ABuTtjcAp7PMk67s2dA1lugiO5dFPTZkqAcA7v64u7e5e1tLS0uGw0qyzv5RzGBVnoINJG7s1AABkWIQZbA5CLSa2WYzKwfuBPbNyrMP2BlGpW0F+kPXWLqy+4BdYX0X8Ey6SoTjDZrZ1jAKbWemMjJ/Z/pHaamtoCyWv9H0a1dUcbrvQt7OJyILF9kkVu4+aWYPAPuBGPCEux8xs/vC/seAZ4HbgHZgBLgnXdlw6IeAp83sXuAEcEfinGb2LlAPlJvZ7cBn3P0N4PPA14Eq4LvhJTnUOTDK2hWVeT3nhsYqXvjJOdwdjWYXWdoinTHR3Z8lHlCS0x5LWnfg/mzLhvTzwK1zlNk0R/oh4Lps6y3zd6b/Apuba/J6zg2NVYxOTHN+eDxvQ65FZGE0g4DkRGf/KGtXVOX1nBsaqwHo6FVXmshSp2AjizY0Nsng6OTMvS/5sqEpHtw6ekfyel4RmT8FG1m0xHNl8n3NZn1DItioZSOy1CnYyKIlgs2aPAebusoyGqrL1LIRKQIKNrJonf3xlkW+WzYQb92oZSOy9CnYyKIlWjb5vmYD8RFpCjYiS5+CjSza6f5RmmrKqSyL5f3cGxqr6egdIT6KXkSWKgUbWbSO3hE2NuZ32HPCppXVjE5Ma/ZnkSVOwUYW7UTPCBuaqgty7itaagF4p2u4IOcXkewo2MiiTE07p3ovcFmBgk1i1oLj3Qo2IkuZgo0sSmf/BSannY2NhQk2a+orqSqLcVwtG5ElTcFGFuVkT3wkWKFaNiUlxqbmGt7pHirI+UUkOwo2signww2VG5sKM0AA4IrmGt5RN5rIkqZgI4tysmeEEoN1DQUMNi01nOy9wPjkdMHqICLpKdjIopzsGWHtiqq8PjRtts3NNUxNOyd61LoRWaoUbGRRTvSMFLQLDeBDq+sAOHpG121ElioFG1kwd+d49zCbm2sLWo+rVtUSKzHe7BwoaD1EZG6RBhsz22ZmR82s3cz2pNhvZvZw2P+amd2YqayZNZnZc2Z2LCwbk/Z9MeQ/amafTUp/IaS9El6ronzfl4rzw+P0jUxw1arCBpvKshhXNNco2IgsYZEFGzOLAY8A24EtwF1mtmVWtu1Aa3jtBh7Nouwe4IC7twIHwjZh/53AtcA24L+G4yTc7e43hNe5XL/fS1H7uXi3VaGDDcCH19Yr2IgsYVG2bG4G2t39uLuPA08BO2bl2QE86XEvAg1mtjZD2R3A3rC+F7g9Kf0pdx9z93eA9nAcichSCzan+0fpGxkvdFVEJIUog8164GTSdkdIyyZPurKr3b0TICwTXWKZzvdnoQvtQTOzVBU2s91mdsjMDnV1dWV6f5e89nND1JTHWFeA59jM9uG18UECb3YOFrgmIpJKlMEm1Q/67Hng58qTTdn5nO9ud78e+GR4/WqqA7j74+7e5u5tLS0tGU4nb3cNceWqWuaI3Xm1ZV09AEdO9xe4JiKSSpTBpgPYmLS9ATidZZ50Zc+GrjbCMnH9Zc4y7n4qLAeBb6LutZxoPzfEVS2F70IDWFVXyfqGKn50orfQVRGRFKIMNgeBVjPbbGblxC/e75uVZx+wM4xK2wr0h66xdGX3AeCqUT4AAA7QSURBVLvC+i7gmaT0O82swsw2Ex908JKZlZpZM4CZlQGfAw5H8YYvJQOjE3T2j3LlErhek9C2qZFD7/bqQWoiS1BpVAd290kzewDYD8SAJ9z9iJndF/Y/BjwL3Eb8Yv4IcE+6suHQDwFPm9m9wAngjlDmiJk9DbwBTAL3u/uUmdUA+0OgiQHPA1+L6n1fKo6cio/8um79igLX5KK2yxt55pXTdPReYGOBJgYVkdQiCzYA7v4s8YCSnPZY0roD92dbNqSfB26do8y/B/79rLRh4Kb51l3SO3wqfm3k2nCtZCm46fImAF5+r1fBRmSJ0QwCsiCHT/ezdkUlzbUVha7KjKvX1FFbUcrBd3sKXRURmUXBRhbk8Kl+rl23dLrQAGIlxtYrmvj7Y926biOyxCjYyLz1j0zwdtcwP7VhaQUbgE9dvYoTPSN6TLTIEqNgI/N26L14N9VHNzUVuCYf9Kmr4/dHfe8tzUgkspQo2Mi8vfRuD2Ux4yOXNRS6Kh+wobGaD62u5e8UbESWFAUbmbeD7/Rw/foVVJbFMmcugM9sWcOLx89zbmC00FURkUDBRualf2SCVzv62XrFykJXZU6/cON6ph2+88qpQldFRAIFG5mXv2/vYmra+blrlu4jga5sqeWGjQ18++VTGpUmskQo2Mi8/N1b52ioLuMjlzVmzlxAv9y2kaNnB3nxuO65EVkKFGwka6MTUzz3xln+j6tXESsp/EzP6fyzG9fTXFvBI99rL3RVRAQFG5mHF46eY3B0kts/MvuxREtPZVmM3/jkZr7f3s1L76h1I1JoCjaStb841EFzbQUfv3LpDg5I9itbL2fdikr+3XdeZ3xyutDVEbmkKdhIVt7uGuLAW+f457dcRmmsOL42NRWl/N87ruMnZ4f4o+d/UujqiFzSiuNXQwruke+1U15aws6PXV7oqszLz29ZzV03b+TRF97mL1/uKHR1RC5ZkT5iQJaHV0728Vc/OsVv/qMrltQsz9n6/X96He+dH+Hf/OWr9AyP8eufuIKSeQ5wGJ+c5p3uYY6eHeS97mG6h8Y4PzzO5FR8aHVlWQktdRWsrq+kdXUdW9bW01JXfJ+VSFQUbCStobFJ/vW3XmF1fQW//XOtha7OgpSXlvDEr32Uf/WtV/gPz77F3x45y2/f2srHr1yZsktwcmqaY+eGeK2jj1dO9vPqyT5+cnaQyemL9+zUV5bSXFtBWSg/MjHJuYExxpKuDa2ur+BjV6zkZ65q5uNXNbO+oSr6NyuyRFmUN72Z2TbgK8SfkPmn7v7QrP0W9t9G/Emdv+buP0pX1syagG8Bm4B3gV92996w74vAvcAU8C/cfX9Ivwn4OlBF/IFsv+MZ3nhbW5sfOnRocR9AkbswPsVvPHmI//12N//917fysSIZGDAXd+cvXu7gy//vUbqHxqirKOX6DStYVVeBA8Njk5zoGeG98yMzQaOuspSf3tDAdetX8OG1dXxodR2bm2tSTtXj7vSNTPDWmUHe6BzglZN9/ODtbrqHxgG4ormGT7Y288nWFrZeuZLaCv1fL1empp3jXUMcPt3PkVMDdPaP0jU0xtDoJCUlECspYWVNOWtWVHJZUzXXrLnY+oz/DEmumNnL7t72gfSogo2ZxYCfAJ8GOoCDwF3u/kZSntuA3yYebG4BvuLut6Qra2ZfBnrc/SEz2wM0uvsXzGwL8OfAzcA64o9//lB4NPRLwO8ALxIPNg+7+3fT1f9SDzY/OtHL7/3V6xw9O8h//KWf5pdu2lDoKuXM2OQUz79xju+3d/PWmQG6h8YoMaOqLMaGxmo2N1ezZV09P72hgU0ra+bd5ZbM3Tl6dpB/aD/P94918eLxHi5MTFFaYtx4eSM/29rMJ1pbuHZd/UwrSdIbnZii/dwQb5we4PDpfg6f6ueNzgFGJ+L/QagoLWF9YxXNNRXUV5XiDhPTTvfgGGcHRjk/PD5zrObaCq5bX89161bEl+tXsL6hSgFoEQoRbD4GfMndPxu2vwjg7v9PUp6vAi+4+5+H7aPAp4i3WlKWTeRx904zWxvKXz37+Ga2H/gS8dbP99z9mpB+Vyj/m+nqfykFm9GJKXqGxznRM8JrHX08/8Y5Xnq3h+baCv7THT/Fp65eulPTFJuxySlefq+Xvz/Wzd8f6+LwqQEAymMltK6uZcvaejY117CuoZJ1K6poqaugrrKMuspSKkpLlv2P4PS0MzQ+ydDoJIOjk5wZGOV03wVO9V7gRM8Ib3YOcLx7mKnQpVlbUcqWde8PFlc016QdMdk/MsGbZwZ4s3OAI6cHOHyqn2PnhmaO2VBdxnXrVvCh1XWsb6xifUMVa1dU0lBdNvO30H8M5jZXsImyHb8eOJm03UG89ZIpz/oMZVe7eydACDiJX8L1xFsus481EdZnp0fi1/ce5J3w4K6ZMO4X1xPB/eI2eNhKxP3k+J9V/veV8aR8aY4R0qamneHxqfe9h9ZVtXxh2zXs/Njl1KirJ6cqSmP8zJXN/MyVzXxh2zWcHxrjB8fP8/qpft44PcD3jp6j++XxlGXLYkZlWYzSEiNWUhKWRmnMiJlBFnEo21CVbVCbDl+0afeZ7+G0+8z3MbGevN9n1p3ppO2paWdk1ncxIVZirKmv5Jo1dXz22jV8eG09H15bt6CW54rqMrZesfJ9k8mOTkzx1plBDp+Kt5QOn+7nmy+9N9Namq2itITyWEn8sy8poSwW/1uUxUrm/oxT7Jgr71yff77+q/E//8UnqCjN7azuUf6SpPpcZjej5sqTTdlsz5f1scxsN7Ab4LLLLstwutQuX1kT/yPZ+ytlZknrc++7WM5S5EuxL6lgqvyJI6c6J0CJGU01ZTTXVrBmRSXXr1/ByiIccVasVtZW8LmfWsfnfmrdTNqF8SlO91/gdN8FusN1h4HRSYbGJrkwPsW0O5PTztRUWE5Pv2/wwlyy7sPIMqPjmBkl4ftrxsw6Sesz6Xbxu27v228z27UVpdRVloZlGavqK1jXUMXquopI7++qLItxw8YGbth48RlN7k7vyASn+y7Q2T/KwIUJBkYnGAx/i4mpaaamnYkpZzKxPsffIVUP0pwf8xw7PPu/4KJZBGEtymDTAWxM2t4AnM4yT3masmfNbG1SN1riKVlzHasjrKerBwDu/jjwOMS70dK9ubk8+LktCykmMqOqPMaVLbVc2VJb6Kpc0syMpppymmrKuW790nsEerGJsuPxINBqZpvNrBy4E9g3K88+YKfFbQX6QxdZurL7gF1hfRfwTFL6nWZWYWabgVbgpXC8QTPbGka/7UwqIyIieRBZy8bdJ83sAWA/8eHLT7j7ETO7L+x/jPjIsNuAduJDn+9JVzYc+iHgaTO7FzgB3BHKHDGzp4E3gEngfndPdAB/notDn78bXiIikieR3mdTzC6l0WgiIrky12g0jd8TEZHIKdiIiEjkFGxERCRyCjYiIhI5BRsREYmcRqPNwcy6gPfCZjPQXcDqzJfqGy3VN1qqb7Siru/l7t4yO1HBJgtmdijVUL6lSvWNluobLdU3WoWqr7rRREQkcgo2IiISOQWb7Dxe6ArMk+obLdU3WqpvtApSX12zERGRyKllIyIikbskg42Z3WFmR8xs2szaZu37opm1m9lRM/tsUvpNZvZ62PdweFwB4ZEG3wrpPzSzTUlldpnZsfDaRQ6Y2ZfM7JSZvRJet0VR93wws22hru1mtief505Rl3fDZ/SKmR0KaU1m9lz4+z1nZo1J+ef1Weegfk+Y2TkzO5yUlrP65fq7MEd9l+R318w2mtn3zOzN8LvwOyF9SX6+aeq7JD/fGe5+yb2ADwNXAy8AbUnpW4BXgQpgM/A2EAv7XgI+Rvxhl98Ftof03wIeC+t3At8K603A8bBsDOuNOaj7l4DfTZGes7rn6W8QC3W8gvjD8l4FthTwO/Eu0Dwr7cvAnrC+B/jDhX7WOajfzwI3AoejqF+uvwtz1HdJfneBtcCNYb0O+Emo05L8fNPUd0l+vonXJdmycfc33f1oil07gKfcfczd3yH+nJ2bLf5E0Hp3/4HHP/0ngduTyuwN638J3Br+d/BZ4Dl373H3XuA5YFuEbyuXdc+Hm4F2dz/u7uPAU6E+S0ny57OX939u8/2sF8Xd/z+gJ8L65fS7MEd951LQ+rp7p7v/KKwPAm8C61min2+a+s6l4N8HuES70dJYD5xM2u4IaevD+uz095Vx90mgH1iZ5li58ICZvRa6KhJN+1zWPR+i/HwWwoG/NbOXzWx3SFvt8Se9EparQvpCPuso5LJ++fouLOnvbugu+gjwQ4rg851VX1jCn++yDTZm9ryZHU7xSve/51SR29OkL7RMWhnq/ihwJXAD0An85wjqng+FPHcqH3f3G4HtwP1m9rNp8ub8b55jS/W7sKS/u2ZWC3wb+JfuPpAu6xznLnR9l/TnG9ljoQvN3X9+AcU6gI1J2xuA0yF9Q4r05DIdZlYKrCDefdABfGpWmReyqUS2dTezrwH/M4K658Nc9S0Idz8dlufM7K+Jd/OdNbO17t4ZuhzOhewL+ayjkMv6Rf5dcPezifWl9t01szLiP9z/3d3/KiQv2c83VX2X8ucLy7hls0D7gDvDSIzNQCvwUmhCD5rZ1tBvuRN4JqlMYqTZLwF/F/o/9wOfMbPG0Jz9TEhblPClT/gFIDHaJ5d1z4eDQKuZbTazcuIXIffl6dzvY2Y1ZlaXWCf+tzrM+z+fXbz/c5vvZx2FXNYv8u/CUv3uhmP/N+BNd/8vSbuW5Oc7V32X6uc7Y7EjDIrxFf4QHcAYcBbYn7Tv3xIfrXGUpJFEQFv4470N/AkXb4itBP6C+EW3l4Arksr8nyG9HbgnR3X/BvA68Fr4QqyNou55+jvcRnwkzdvAvy3g9+EK4qN1XgWOJOpCvI/6AHAsLJsW+lnnoI5/TrxrZCJ8d+/NZf1y/V2Yo75L8rsLfIJ4F9FrwCvhddtS/XzT1HdJfr6Jl2YQEBGRyKkbTUREIqdgIyIikVOwERGRyCnYiIhI5BRsREQkcgo2IiISOQUbERGJnIKNiIhE7v8HpYjPYt/TTvwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts of CLASSIFICATION\n",
    "CLASS_column_Series.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "Other     1484\n",
       "C7000      777\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace if counts are less than ..?\n",
    "replace_CLASS_list = CLASS_column_Series[CLASS_column_Series < 500].index.tolist()\n",
    "\n",
    "# Replace in dataframe\n",
    "for class_type in replace_CLASS_list:\n",
    "     df.CLASSIFICATION = df.CLASSIFICATION.replace(class_type,'Other')\n",
    "    \n",
    "# Check to make sure binning was successful\n",
    "df.CLASSIFICATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable lists\n",
    "cat_name_list = df.dtypes[df.dtypes == \"object\"].index.tolist()\n",
    "cat_name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APPLICATION_TYPE_Others</th>\n",
       "      <th>APPLICATION_TYPE_T10</th>\n",
       "      <th>APPLICATION_TYPE_T19</th>\n",
       "      <th>APPLICATION_TYPE_T3</th>\n",
       "      <th>APPLICATION_TYPE_T4</th>\n",
       "      <th>APPLICATION_TYPE_T5</th>\n",
       "      <th>APPLICATION_TYPE_T6</th>\n",
       "      <th>APPLICATION_TYPE_T7</th>\n",
       "      <th>APPLICATION_TYPE_T8</th>\n",
       "      <th>AFFILIATION_CompanySponsored</th>\n",
       "      <th>...</th>\n",
       "      <th>INCOME_AMT_1-9999</th>\n",
       "      <th>INCOME_AMT_10000-24999</th>\n",
       "      <th>INCOME_AMT_100000-499999</th>\n",
       "      <th>INCOME_AMT_10M-50M</th>\n",
       "      <th>INCOME_AMT_1M-5M</th>\n",
       "      <th>INCOME_AMT_25000-99999</th>\n",
       "      <th>INCOME_AMT_50M+</th>\n",
       "      <th>INCOME_AMT_5M-10M</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_N</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   APPLICATION_TYPE_Others  APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  \\\n",
       "0                      0.0                   1.0                   0.0   \n",
       "1                      0.0                   0.0                   0.0   \n",
       "2                      0.0                   0.0                   0.0   \n",
       "3                      0.0                   0.0                   0.0   \n",
       "4                      0.0                   0.0                   0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T3  APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  1.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  1.0   \n",
       "3                  1.0                  0.0                  0.0   \n",
       "4                  1.0                  0.0                  0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T6  APPLICATION_TYPE_T7  APPLICATION_TYPE_T8  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   AFFILIATION_CompanySponsored  ...  INCOME_AMT_1-9999  \\\n",
       "0                           0.0  ...                0.0   \n",
       "1                           0.0  ...                1.0   \n",
       "2                           1.0  ...                0.0   \n",
       "3                           1.0  ...                0.0   \n",
       "4                           0.0  ...                0.0   \n",
       "\n",
       "   INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  INCOME_AMT_10M-50M  \\\n",
       "0                     0.0                       0.0                 0.0   \n",
       "1                     0.0                       0.0                 0.0   \n",
       "2                     0.0                       0.0                 0.0   \n",
       "3                     1.0                       0.0                 0.0   \n",
       "4                     0.0                       1.0                 0.0   \n",
       "\n",
       "   INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  INCOME_AMT_50M+  \\\n",
       "0               0.0                     0.0              0.0   \n",
       "1               0.0                     0.0              0.0   \n",
       "2               0.0                     0.0              0.0   \n",
       "3               0.0                     0.0              0.0   \n",
       "4               0.0                     0.0              0.0   \n",
       "\n",
       "   INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y  \n",
       "0                0.0                       1.0                       0.0  \n",
       "1                0.0                       1.0                       0.0  \n",
       "2                0.0                       1.0                       0.0  \n",
       "3                0.0                       1.0                       0.0  \n",
       "4                0.0                       1.0                       0.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False) \n",
    "\n",
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(df[cat_name_list]))\n",
    "\n",
    "# Add the encoded variable names to the dataframe\n",
    "encode_df.columns = enc.get_feature_names(cat_name_list)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATUS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "      <th>APPLICATION_TYPE_Others</th>\n",
       "      <th>APPLICATION_TYPE_T10</th>\n",
       "      <th>APPLICATION_TYPE_T19</th>\n",
       "      <th>APPLICATION_TYPE_T3</th>\n",
       "      <th>APPLICATION_TYPE_T4</th>\n",
       "      <th>APPLICATION_TYPE_T5</th>\n",
       "      <th>APPLICATION_TYPE_T6</th>\n",
       "      <th>...</th>\n",
       "      <th>INCOME_AMT_1-9999</th>\n",
       "      <th>INCOME_AMT_10000-24999</th>\n",
       "      <th>INCOME_AMT_100000-499999</th>\n",
       "      <th>INCOME_AMT_10M-50M</th>\n",
       "      <th>INCOME_AMT_1M-5M</th>\n",
       "      <th>INCOME_AMT_25000-99999</th>\n",
       "      <th>INCOME_AMT_50M+</th>\n",
       "      <th>INCOME_AMT_5M-10M</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_N</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>108590</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6692</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>142590</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATUS  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Others  \\\n",
       "0       1     5000              1                      0.0   \n",
       "1       1   108590              1                      0.0   \n",
       "2       1     5000              0                      0.0   \n",
       "3       1     6692              1                      0.0   \n",
       "4       1   142590              1                      0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T10  APPLICATION_TYPE_T19  APPLICATION_TYPE_T3  \\\n",
       "0                   1.0                   0.0                  0.0   \n",
       "1                   0.0                   0.0                  1.0   \n",
       "2                   0.0                   0.0                  0.0   \n",
       "3                   0.0                   0.0                  1.0   \n",
       "4                   0.0                   0.0                  1.0   \n",
       "\n",
       "   APPLICATION_TYPE_T4  APPLICATION_TYPE_T5  APPLICATION_TYPE_T6  ...  \\\n",
       "0                  0.0                  0.0                  0.0  ...   \n",
       "1                  0.0                  0.0                  0.0  ...   \n",
       "2                  0.0                  1.0                  0.0  ...   \n",
       "3                  0.0                  0.0                  0.0  ...   \n",
       "4                  0.0                  0.0                  0.0  ...   \n",
       "\n",
       "   INCOME_AMT_1-9999  INCOME_AMT_10000-24999  INCOME_AMT_100000-499999  \\\n",
       "0                0.0                     0.0                       0.0   \n",
       "1                1.0                     0.0                       0.0   \n",
       "2                0.0                     0.0                       0.0   \n",
       "3                0.0                     1.0                       0.0   \n",
       "4                0.0                     0.0                       1.0   \n",
       "\n",
       "   INCOME_AMT_10M-50M  INCOME_AMT_1M-5M  INCOME_AMT_25000-99999  \\\n",
       "0                 0.0               0.0                     0.0   \n",
       "1                 0.0               0.0                     0.0   \n",
       "2                 0.0               0.0                     0.0   \n",
       "3                 0.0               0.0                     0.0   \n",
       "4                 0.0               0.0                     0.0   \n",
       "\n",
       "   INCOME_AMT_50M+  INCOME_AMT_5M-10M  SPECIAL_CONSIDERATIONS_N  \\\n",
       "0              0.0                0.0                       1.0   \n",
       "1              0.0                0.0                       1.0   \n",
       "2              0.0                0.0                       1.0   \n",
       "3              0.0                0.0                       1.0   \n",
       "4              0.0                0.0                       1.0   \n",
       "\n",
       "   SPECIAL_CONSIDERATIONS_Y  \n",
       "0                       0.0  \n",
       "1                       0.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "df = df.merge(encode_df, left_index = True, right_index = True)\n",
    "df = df.drop(columns = cat_name_list)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = df[\"IS_SUCCESSFUL\"].values\n",
    "\n",
    "X = 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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instances\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 30)                1350      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 20)                620       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                210       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 2,191\n",
      "Trainable params: 2,191\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "### OPTIMIZE\n",
    "# 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 = 30\n",
    "hidden_nodes_layer2 = 20\n",
    "hidden_nodes_layer3 = 10\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=\"sigmoid\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"sigmoid\"))\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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Create a callback that saves the model's weights every 5 epochs\n",
    "cp_callback = ModelCheckpoint(\n",
    "    filepath=checkpoint_path,\n",
    "    verbose=1,\n",
    "    save_weights_only=True,\n",
    "    save_freq=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "804/804 [==============================] - 0s 604us/step - loss: 0.6195 - accuracy: 0.6838\n",
      "Epoch 2/50\n",
      "149/804 [====>.........................] - ETA: 0s - loss: 0.5734 - accuracy: 0.7280\n",
      "Epoch 00002: saving model to checkpoints\\weights.02.hdf5\n",
      "804/804 [==============================] - 1s 628us/step - loss: 0.5720 - accuracy: 0.7255\n",
      "Epoch 3/50\n",
      "350/804 [============>.................] - ETA: 0s - loss: 0.5685 - accuracy: 0.7255\n",
      "Epoch 00003: saving model to checkpoints\\weights.03.hdf5\n",
      "804/804 [==============================] - 0s 617us/step - loss: 0.5661 - accuracy: 0.7285\n",
      "Epoch 4/50\n",
      "522/804 [==================>...........] - ETA: 0s - loss: 0.5656 - accuracy: 0.7261\n",
      "Epoch 00004: saving model to checkpoints\\weights.04.hdf5\n",
      "804/804 [==============================] - 0s 607us/step - loss: 0.5612 - accuracy: 0.7294\n",
      "Epoch 5/50\n",
      "766/804 [===========================>..] - ETA: 0s - loss: 0.5571 - accuracy: 0.7315\n",
      "Epoch 00005: saving model to checkpoints\\weights.05.hdf5\n",
      "804/804 [==============================] - 0s 617us/step - loss: 0.5577 - accuracy: 0.7310\n",
      "Epoch 6/50\n",
      "804/804 [==============================] - 0s 598us/step - loss: 0.5561 - accuracy: 0.7311\n",
      "Epoch 7/50\n",
      "130/804 [===>..........................] - ETA: 0s - loss: 0.5520 - accuracy: 0.7341\n",
      "Epoch 00007: saving model to checkpoints\\weights.07.hdf5\n",
      "804/804 [==============================] - 1s 656us/step - loss: 0.5544 - accuracy: 0.7317\n",
      "Epoch 8/50\n",
      "308/804 [==========>...................] - ETA: 0s - loss: 0.5479 - accuracy: 0.7334\n",
      "Epoch 00008: saving model to checkpoints\\weights.08.hdf5\n",
      "804/804 [==============================] - 1s 695us/step - loss: 0.5534 - accuracy: 0.7316\n",
      "Epoch 9/50\n",
      "564/804 [====================>.........] - ETA: 0s - loss: 0.5523 - accuracy: 0.7345\n",
      "Epoch 00009: saving model to checkpoints\\weights.09.hdf5\n",
      "804/804 [==============================] - 1s 645us/step - loss: 0.5524 - accuracy: 0.7328\n",
      "Epoch 10/50\n",
      "726/804 [==========================>...] - ETA: 0s - loss: 0.5531 - accuracy: 0.7309\n",
      "Epoch 00010: saving model to checkpoints\\weights.10.hdf5\n",
      "804/804 [==============================] - 1s 644us/step - loss: 0.5514 - accuracy: 0.7324\n",
      "Epoch 11/50\n",
      "804/804 [==============================] - 0s 616us/step - loss: 0.5506 - accuracy: 0.7319\n",
      "Epoch 12/50\n",
      "139/804 [====>.........................] - ETA: 0s - loss: 0.5454 - accuracy: 0.7361\n",
      "Epoch 00012: saving model to checkpoints\\weights.12.hdf5\n",
      "804/804 [==============================] - 1s 663us/step - loss: 0.5500 - accuracy: 0.7320\n",
      "Epoch 13/50\n",
      "273/804 [=========>....................] - ETA: 0s - loss: 0.5469 - accuracy: 0.7355\n",
      "Epoch 00013: saving model to checkpoints\\weights.13.hdf5\n",
      "804/804 [==============================] - 1s 683us/step - loss: 0.5494 - accuracy: 0.7336\n",
      "Epoch 14/50\n",
      "480/804 [================>.............] - ETA: 0s - loss: 0.5472 - accuracy: 0.7359\n",
      "Epoch 00014: saving model to checkpoints\\weights.14.hdf5\n",
      "804/804 [==============================] - 1s 780us/step - loss: 0.5488 - accuracy: 0.7339\n",
      "Epoch 15/50\n",
      "699/804 [=========================>....] - ETA: 0s - loss: 0.5467 - accuracy: 0.7354\n",
      "Epoch 00015: saving model to checkpoints\\weights.15.hdf5\n",
      "804/804 [==============================] - 1s 890us/step - loss: 0.5482 - accuracy: 0.7345\n",
      "Epoch 16/50\n",
      "804/804 [==============================] - 1s 840us/step - loss: 0.5479 - accuracy: 0.7331\n",
      "Epoch 17/50\n",
      " 71/804 [=>............................] - ETA: 0s - loss: 0.5393 - accuracy: 0.7328\n",
      "Epoch 00017: saving model to checkpoints\\weights.17.hdf5\n",
      "804/804 [==============================] - 1s 672us/step - loss: 0.5476 - accuracy: 0.7336\n",
      "Epoch 18/50\n",
      "317/804 [==========>...................] - ETA: 0s - loss: 0.5424 - accuracy: 0.7378\n",
      "Epoch 00018: saving model to checkpoints\\weights.18.hdf5\n",
      "804/804 [==============================] - 1s 656us/step - loss: 0.5470 - accuracy: 0.7337\n",
      "Epoch 19/50\n",
      "482/804 [================>.............] - ETA: 0s - loss: 0.5453 - accuracy: 0.7355\n",
      "Epoch 00019: saving model to checkpoints\\weights.19.hdf5\n",
      "804/804 [==============================] - 1s 677us/step - loss: 0.5466 - accuracy: 0.7342\n",
      "Epoch 20/50\n",
      "684/804 [========================>.....] - ETA: 0s - loss: 0.5460 - accuracy: 0.7338\n",
      "Epoch 00020: saving model to checkpoints\\weights.20.hdf5\n",
      "804/804 [==============================] - 1s 854us/step - loss: 0.5467 - accuracy: 0.7340\n",
      "Epoch 21/50\n",
      "804/804 [==============================] - 1s 662us/step - loss: 0.5460 - accuracy: 0.7341\n",
      "Epoch 22/50\n",
      " 63/804 [=>............................] - ETA: 0s - loss: 0.5485 - accuracy: 0.7297\n",
      "Epoch 00022: saving model to checkpoints\\weights.22.hdf5\n",
      "804/804 [==============================] - 1s 693us/step - loss: 0.5457 - accuracy: 0.7344\n",
      "Epoch 23/50\n",
      "241/804 [=======>......................] - ETA: 0s - loss: 0.5409 - accuracy: 0.7376\n",
      "Epoch 00023: saving model to checkpoints\\weights.23.hdf5\n",
      "804/804 [==============================] - 1s 638us/step - loss: 0.5457 - accuracy: 0.7343\n",
      "Epoch 24/50\n",
      "485/804 [=================>............] - ETA: 0s - loss: 0.5436 - accuracy: 0.7366\n",
      "Epoch 00024: saving model to checkpoints\\weights.24.hdf5\n",
      "804/804 [==============================] - 1s 643us/step - loss: 0.5456 - accuracy: 0.7350\n",
      "Epoch 25/50\n",
      "658/804 [=======================>......] - ETA: 0s - loss: 0.5462 - accuracy: 0.7336\n",
      "Epoch 00025: saving model to checkpoints\\weights.25.hdf5\n",
      "804/804 [==============================] - 1s 640us/step - loss: 0.5452 - accuracy: 0.7343\n",
      "Epoch 26/50\n",
      "804/804 [==============================] - 1s 643us/step - loss: 0.5451 - accuracy: 0.7353\n",
      "Epoch 27/50\n",
      " 73/804 [=>............................] - ETA: 0s - loss: 0.5508 - accuracy: 0.7329\n",
      "Epoch 00027: saving model to checkpoints\\weights.27.hdf5\n",
      "804/804 [==============================] - 1s 802us/step - loss: 0.5448 - accuracy: 0.7353\n",
      "Epoch 28/50\n",
      "229/804 [=======>......................] - ETA: 0s - loss: 0.5430 - accuracy: 0.7305\n",
      "Epoch 00028: saving model to checkpoints\\weights.28.hdf5\n",
      "804/804 [==============================] - 1s 727us/step - loss: 0.5443 - accuracy: 0.7346\n",
      "Epoch 29/50\n",
      "465/804 [================>.............] - ETA: 0s - loss: 0.5423 - accuracy: 0.7370\n",
      "Epoch 00029: saving model to checkpoints\\weights.29.hdf5\n",
      "804/804 [==============================] - 1s 688us/step - loss: 0.5442 - accuracy: 0.7351\n",
      "Epoch 30/50\n",
      "681/804 [========================>.....] - ETA: 0s - loss: 0.5454 - accuracy: 0.7348\n",
      "Epoch 00030: saving model to checkpoints\\weights.30.hdf5\n",
      "804/804 [==============================] - 1s 670us/step - loss: 0.5444 - accuracy: 0.7356\n",
      "Epoch 31/50\n",
      "804/804 [==============================] - 1s 652us/step - loss: 0.5441 - accuracy: 0.7355\n",
      "Epoch 32/50\n",
      " 72/804 [=>............................] - ETA: 0s - loss: 0.5409 - accuracy: 0.7409\n",
      "Epoch 00032: saving model to checkpoints\\weights.32.hdf5\n",
      "804/804 [==============================] - 1s 644us/step - loss: 0.5441 - accuracy: 0.7350\n",
      "Epoch 33/50\n",
      "239/804 [=======>......................] - ETA: 0s - loss: 0.5471 - accuracy: 0.7364\n",
      "Epoch 00033: saving model to checkpoints\\weights.33.hdf5\n",
      "804/804 [==============================] - 1s 676us/step - loss: 0.5438 - accuracy: 0.7359\n",
      "Epoch 34/50\n",
      "386/804 [=============>................] - ETA: 0s - loss: 0.5428 - accuracy: 0.7358\n",
      "Epoch 00034: saving model to checkpoints\\weights.34.hdf5\n",
      "804/804 [==============================] - 1s 643us/step - loss: 0.5434 - accuracy: 0.7352\n",
      "Epoch 35/50\n",
      "647/804 [=======================>......] - ETA: 0s - loss: 0.5439 - accuracy: 0.7364\n",
      "Epoch 00035: saving model to checkpoints\\weights.35.hdf5\n",
      "804/804 [==============================] - 1s 664us/step - loss: 0.5435 - accuracy: 0.7365\n",
      "Epoch 36/50\n",
      "804/804 [==============================] - 1s 651us/step - loss: 0.5434 - accuracy: 0.7350\n",
      "Epoch 37/50\n",
      "  1/804 [..............................] - ETA: 0s - loss: 0.4573 - accuracy: 0.7812\n",
      "Epoch 00037: saving model to checkpoints\\weights.37.hdf5\n",
      "804/804 [==============================] - 1s 690us/step - loss: 0.5431 - accuracy: 0.7354\n",
      "Epoch 38/50\n",
      "230/804 [=======>......................] - ETA: 0s - loss: 0.5451 - accuracy: 0.7289\n",
      "Epoch 00038: saving model to checkpoints\\weights.38.hdf5\n",
      "804/804 [==============================] - 1s 674us/step - loss: 0.5433 - accuracy: 0.7362\n",
      "Epoch 39/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "385/804 [=============>................] - ETA: 0s - loss: 0.5393 - accuracy: 0.7413\n",
      "Epoch 00039: saving model to checkpoints\\weights.39.hdf5\n",
      "804/804 [==============================] - 1s 680us/step - loss: 0.5427 - accuracy: 0.7364\n",
      "Epoch 40/50\n",
      "586/804 [====================>.........] - ETA: 0s - loss: 0.5432 - accuracy: 0.7375\n",
      "Epoch 00040: saving model to checkpoints\\weights.40.hdf5\n",
      "804/804 [==============================] - 1s 627us/step - loss: 0.5430 - accuracy: 0.7366\n",
      "Epoch 41/50\n",
      "804/804 [==============================] - 0s 598us/step - loss: 0.5426 - accuracy: 0.7368\n",
      "Epoch 42/50\n",
      "  1/804 [..............................] - ETA: 0s - loss: 0.5081 - accuracy: 0.7500\n",
      "Epoch 00042: saving model to checkpoints\\weights.42.hdf5\n",
      "804/804 [==============================] - 1s 652us/step - loss: 0.5428 - accuracy: 0.7360\n",
      "Epoch 43/50\n",
      "221/804 [=======>......................] - ETA: 0s - loss: 0.5490 - accuracy: 0.7353\n",
      "Epoch 00043: saving model to checkpoints\\weights.43.hdf5\n",
      "804/804 [==============================] - 1s 642us/step - loss: 0.5427 - accuracy: 0.7364\n",
      "Epoch 44/50\n",
      "425/804 [==============>...............] - ETA: 0s - loss: 0.5434 - accuracy: 0.7386\n",
      "Epoch 00044: saving model to checkpoints\\weights.44.hdf5\n",
      "804/804 [==============================] - 0s 613us/step - loss: 0.5420 - accuracy: 0.7375\n",
      "Epoch 45/50\n",
      "570/804 [====================>.........] - ETA: 0s - loss: 0.5435 - accuracy: 0.7356\n",
      "Epoch 00045: saving model to checkpoints\\weights.45.hdf5\n",
      "804/804 [==============================] - 1s 650us/step - loss: 0.5427 - accuracy: 0.7370\n",
      "Epoch 46/50\n",
      "804/804 [==============================] - 1s 635us/step - loss: 0.5419 - accuracy: 0.7375\n",
      "Epoch 47/50\n",
      "  1/804 [..............................] - ETA: 0s - loss: 0.5218 - accuracy: 0.7812\n",
      "Epoch 00047: saving model to checkpoints\\weights.47.hdf5\n",
      "804/804 [==============================] - 0s 609us/step - loss: 0.5419 - accuracy: 0.7368\n",
      "Epoch 48/50\n",
      "144/804 [====>.........................] - ETA: 0s - loss: 0.5420 - accuracy: 0.7318\n",
      "Epoch 00048: saving model to checkpoints\\weights.48.hdf5\n",
      "804/804 [==============================] - 1s 676us/step - loss: 0.5417 - accuracy: 0.7376\n",
      "Epoch 49/50\n",
      "396/804 [=============>................] - ETA: 0s - loss: 0.5440 - accuracy: 0.7350\n",
      "Epoch 00049: saving model to checkpoints\\weights.49.hdf5\n",
      "804/804 [==============================] - 1s 639us/step - loss: 0.5420 - accuracy: 0.7377\n",
      "Epoch 50/50\n",
      "588/804 [====================>.........] - ETA: 0s - loss: 0.5434 - accuracy: 0.7374\n",
      "Epoch 00050: saving model to checkpoints\\weights.50.hdf5\n",
      "804/804 [==============================] - 1s 633us/step - loss: 0.5417 - accuracy: 0.7372\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn.fit(X_train_scaled ,y_train,epochs=50, callbacks=[cp_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 0s - loss: 0.5505 - accuracy: 0.7272\n",
      "Loss: 0.5504606366157532, Accuracy: 0.7272303104400635\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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