Investment_Prediction_Deep_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import & Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in dataset\n",
    "charity_data_df = pd.read_csv(\"Resources/charity_data.csv\")\n",
    "charity_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns from data df that will not impact the learning model \n",
    "charity_data_df = charity_data_df.drop(columns=['EIN'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please note: For the data processing phase, different binning amounts were tested in an attempt to improve model accuracy. The different binning thresholds can be seen in the commented out lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate list of columns with categorical variables\n",
    "charity_cat = charity_data_df.dtypes[charity_data_df.dtypes == \"object\"].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of unique values in each column\n",
    "charity_data_df[charity_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
       "ROCKY FORD ROTARY CLUB                                    1\n",
       "WICHITA FALLS MINISTRY AT THE WELL                        1\n",
       "LOUIS SMITHHISTLER COUNCIL 9504 KNIGHTS OF COLUMBUS       1\n",
       "GARY CARTER FOUNDATION INC                                1\n",
       "CHRISTMAS IN APRIL-NATIONAL CITY                          1\n",
       "Name: NAME, Length: 19568, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine number of each type of value in Name column\n",
    "name_counts = charity_data_df.NAME.value_counts()\n",
    "name_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                                              29662\n",
       "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",
       "PTA TEXAS CONGRESS                                   368\n",
       "SOROPTIMIST INTERNATIONAL OF THE AMERICAS INC        331\n",
       "ALPHA PHI SIGMA                                      313\n",
       "Name: NAME, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace\n",
    "replace_name_counts = list(name_counts[name_counts < 300].index)\n",
    "#replace_name_counts = list(name_counts[name_counts < 500].index)\n",
    "\n",
    "# Replace in DataFrame\n",
    "for name_count in replace_name_counts:\n",
    "    charity_data_df['NAME'] = charity_data_df['NAME'].replace(name_count,'Other')\n",
    "\n",
    "# Check to make sure binning was successful\n",
    "charity_data_df['NAME'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3     27037\n",
       "T4      1542\n",
       "T6      1216\n",
       "T5      1173\n",
       "T19     1065\n",
       "T8       737\n",
       "T7       725\n",
       "T10      528\n",
       "T9       156\n",
       "T13       66\n",
       "T12       27\n",
       "T2        16\n",
       "T14        3\n",
       "T25        3\n",
       "T15        2\n",
       "T29        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine number of each type of value in Application_type column\n",
    "app_counts = charity_data_df.APPLICATION_TYPE.value_counts()\n",
    "app_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3       27037\n",
       "T4        1542\n",
       "T6        1216\n",
       "T5        1173\n",
       "T19       1065\n",
       "T8         737\n",
       "T7         725\n",
       "T10        528\n",
       "Other      276\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace\n",
    "replace_app_counts = list(app_counts[app_counts < 500].index)\n",
    "#replace_app_counts = list(app_counts[app_counts < 1000].index)\n",
    "\n",
    "# Replace in DataFrame\n",
    "for app_count in replace_app_counts:\n",
    "    charity_data_df['APPLICATION_TYPE'] = charity_data_df['APPLICATION_TYPE'].replace(app_count,'Other')\n",
    "\n",
    "# Check to make sure binning was successful\n",
    "charity_data_df['APPLICATION_TYPE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "         ...  \n",
       "C1570        1\n",
       "C3700        1\n",
       "C2150        1\n",
       "C6100        1\n",
       "C1248        1\n",
       "Name: CLASSIFICATION, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine number of each type of value in CLASSIFICATION column\n",
    "class_counts = charity_data_df.CLASSIFICATION.value_counts()\n",
    "class_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which values to replace\n",
    "replace_class_counts = list(class_counts[class_counts < 500].index)\n",
    "#replace_class_counts = list(class_counts[class_counts < 1500].index)\n",
    "\n",
    "# Replace in DataFrame\n",
    "for class_count in replace_class_counts:\n",
    "    charity_data_df['CLASSIFICATION'] = charity_data_df['CLASSIFICATION'].replace(class_count,'Other')\n",
    "\n",
    "# Check to make sure binning was successful\n",
    "charity_data_df['CLASSIFICATION'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encode, Split, & Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_ALPHA PHI SIGMA</th>\n",
       "      <th>NAME_AMATEUR ATHLETIC UNION OF THE UNITED STATES INC</th>\n",
       "      <th>NAME_Other</th>\n",
       "      <th>NAME_PARENT BOOSTER USA INC</th>\n",
       "      <th>NAME_PTA TEXAS CONGRESS</th>\n",
       "      <th>NAME_SOROPTIMIST INTERNATIONAL OF THE AMERICAS INC</th>\n",
       "      <th>NAME_TOPS CLUB INC</th>\n",
       "      <th>NAME_UNITED STATES BOWLING CONGRESS INC</th>\n",
       "      <th>NAME_WASHINGTON STATE UNIVERSITY</th>\n",
       "      <th>APPLICATION_TYPE_Other</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",
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       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NAME_ALPHA PHI SIGMA  NAME_AMATEUR ATHLETIC UNION OF THE UNITED STATES 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_Other  NAME_PARENT BOOSTER USA INC  NAME_PTA TEXAS CONGRESS  \\\n",
       "0         1.0                          0.0                      0.0   \n",
       "1         1.0                          0.0                      0.0   \n",
       "2         1.0                          0.0                      0.0   \n",
       "3         1.0                          0.0                      0.0   \n",
       "4         1.0                          0.0                      0.0   \n",
       "\n",
       "   NAME_SOROPTIMIST INTERNATIONAL OF THE AMERICAS INC  NAME_TOPS CLUB 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_UNITED STATES BOWLING CONGRESS INC  NAME_WASHINGTON STATE UNIVERSITY  \\\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",
       "   APPLICATION_TYPE_Other  ...  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",
       "2                     0.0  ...                0.0                     0.0   \n",
       "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 51 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the 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(charity_data_df[charity_cat]))\n",
    "\n",
    "# Add the encoded variable names to the DataFrame\n",
    "encode_df.columns = enc.get_feature_names(charity_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATUS</th>\n",
       "      <th>ASK_AMT</th>\n",
       "      <th>IS_SUCCESSFUL</th>\n",
       "      <th>NAME_ALPHA PHI SIGMA</th>\n",
       "      <th>NAME_AMATEUR ATHLETIC UNION OF THE UNITED STATES INC</th>\n",
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       "      <th>NAME_SOROPTIMIST INTERNATIONAL OF THE AMERICAS INC</th>\n",
       "      <th>NAME_TOPS CLUB INC</th>\n",
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      ],
      "text/plain": [
       "   STATUS  ASK_AMT  IS_SUCCESSFUL  NAME_ALPHA PHI SIGMA  \\\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",
       "   NAME_AMATEUR ATHLETIC UNION OF THE UNITED STATES INC  NAME_Other  \\\n",
       "0                                                0.0            1.0   \n",
       "1                                                0.0            1.0   \n",
       "2                                                0.0            1.0   \n",
       "3                                                0.0            1.0   \n",
       "4                                                0.0            1.0   \n",
       "\n",
       "   NAME_PARENT BOOSTER USA INC  NAME_PTA TEXAS CONGRESS  \\\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_SOROPTIMIST INTERNATIONAL OF THE AMERICAS INC  NAME_TOPS CLUB 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",
       "   ...  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 54 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "charity_data_df = charity_data_df.merge(encode_df,left_index=True, right_index=True)\n",
    "charity_data_df = charity_data_df.drop(charity_cat,1)\n",
    "charity_data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split preprocessed data into features and target arrays\n",
    "y = charity_data_df[\"IS_SUCCESSFUL\"].values\n",
    "X = charity_data_df.drop([\"IS_SUCCESSFUL\"],1).values\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instance\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": [
    "# Create Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Please Note: During the model creation and testing phases, several different configurations were tested in an attempt to improve model accuracy. At first, a single neural layer network was tested. Different testing layers, node amounts, activation functions, and epochs were tested. They can be seen in the commented out lines in each section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 10)                540       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 55        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 601\n",
      "Trainable params: 601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "number_input_features = len(X_train[0])\n",
    "hidden_nodes_layer1 =  10\n",
    "#hidden_nodes_layer1 =  150\n",
    "#hidden_nodes_layer1 =  50\n",
    "hidden_nodes_layer2 = 5\n",
    "#hidden_nodes_layer2 = 100\n",
    "#hidden_nodes_layer2 = 25\n",
    "#hidden_nodes_layer3 = 10\n",
    "\n",
    "nn_model = tf.keras.models.Sequential()\n",
    "\n",
    "# Add first layer to the Sequential model using Keras’ Dense class\n",
    "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "#nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"tanh\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "#nn_model.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Add the output layer that uses a probability activation function\n",
    "nn_model.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the Sequential model\n",
    "nn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the Sequential model together and customize metrics\n",
    "nn_model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and Test Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/80\n",
      "25724/25724 [==============================] - 2s 76us/sample - loss: 0.5801 - accuracy: 0.6942\n",
      "Epoch 2/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5338 - accuracy: 0.7322\n",
      "Epoch 3/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5267 - accuracy: 0.7372\n",
      "Epoch 4/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5231 - accuracy: 0.7400\n",
      "Epoch 5/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5210 - accuracy: 0.7410\n",
      "Epoch 6/80\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5196 - accuracy: 0.7411\n",
      "Epoch 7/80\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5184 - accuracy: 0.7431\n",
      "Epoch 8/80\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5180 - accuracy: 0.7430\n",
      "Epoch 9/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5172 - accuracy: 0.7436\n",
      "Epoch 10/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5166 - accuracy: 0.7441\n",
      "Epoch 11/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5159 - accuracy: 0.7449\n",
      "Epoch 12/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5152 - accuracy: 0.7439\n",
      "Epoch 13/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5152 - accuracy: 0.7451\n",
      "Epoch 14/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5150 - accuracy: 0.7456\n",
      "Epoch 15/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5143 - accuracy: 0.7462\n",
      "Epoch 16/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5144 - accuracy: 0.7458\n",
      "Epoch 17/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5142 - accuracy: 0.7461\n",
      "Epoch 18/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5139 - accuracy: 0.7463\n",
      "Epoch 19/80\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5137 - accuracy: 0.7463\n",
      "Epoch 20/80\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5134 - accuracy: 0.7467\n",
      "Epoch 21/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5133 - accuracy: 0.7461\n",
      "Epoch 22/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5128 - accuracy: 0.7470\n",
      "Epoch 23/80\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5130 - accuracy: 0.7459\n",
      "Epoch 24/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5128 - accuracy: 0.7473\n",
      "Epoch 25/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5126 - accuracy: 0.7469\n",
      "Epoch 26/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5124 - accuracy: 0.7470\n",
      "Epoch 27/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5126 - accuracy: 0.7473\n",
      "Epoch 28/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5119 - accuracy: 0.7474\n",
      "Epoch 29/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5117 - accuracy: 0.7477\n",
      "Epoch 30/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5119 - accuracy: 0.7469\n",
      "Epoch 31/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5118 - accuracy: 0.7477\n",
      "Epoch 32/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5115 - accuracy: 0.7475\n",
      "Epoch 33/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5117 - accuracy: 0.7477\n",
      "Epoch 34/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5114 - accuracy: 0.7478\n",
      "Epoch 35/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5109 - accuracy: 0.7482\n",
      "Epoch 36/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5111 - accuracy: 0.7479\n",
      "Epoch 37/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5113 - accuracy: 0.7477\n",
      "Epoch 38/80\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5109 - accuracy: 0.7476\n",
      "Epoch 39/80\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5108 - accuracy: 0.7473\n",
      "Epoch 40/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5107 - accuracy: 0.7482\n",
      "Epoch 41/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5108 - accuracy: 0.7483\n",
      "Epoch 42/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5104 - accuracy: 0.7482\n",
      "Epoch 43/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5104 - accuracy: 0.7481\n",
      "Epoch 44/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5104 - accuracy: 0.7483\n",
      "Epoch 45/80\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5103 - accuracy: 0.7483\n",
      "Epoch 46/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5104 - accuracy: 0.7481\n",
      "Epoch 47/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5103 - accuracy: 0.7483\n",
      "Epoch 48/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5098 - accuracy: 0.7488\n",
      "Epoch 49/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5099 - accuracy: 0.7472\n",
      "Epoch 50/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5100 - accuracy: 0.7489\n",
      "Epoch 51/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5099 - accuracy: 0.7475\n",
      "Epoch 52/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5096 - accuracy: 0.7483\n",
      "Epoch 53/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5100 - accuracy: 0.7485\n",
      "Epoch 54/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5094 - accuracy: 0.7497\n",
      "Epoch 55/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5099 - accuracy: 0.7485\n",
      "Epoch 56/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5098 - accuracy: 0.7483\n",
      "Epoch 57/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5096 - accuracy: 0.7482\n",
      "Epoch 58/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5093 - accuracy: 0.7484\n",
      "Epoch 59/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5094 - accuracy: 0.7470\n",
      "Epoch 60/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5091 - accuracy: 0.7481\n",
      "Epoch 61/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5093 - accuracy: 0.7486\n",
      "Epoch 62/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5091 - accuracy: 0.7478\n",
      "Epoch 63/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5093 - accuracy: 0.7484\n",
      "Epoch 64/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5094 - accuracy: 0.7487\n",
      "Epoch 65/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5090 - accuracy: 0.7483\n",
      "Epoch 66/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5093 - accuracy: 0.7488\n",
      "Epoch 67/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5092 - accuracy: 0.7487\n",
      "Epoch 68/80\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5089 - accuracy: 0.7488\n",
      "Epoch 69/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5089 - accuracy: 0.7482\n",
      "Epoch 70/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5091 - accuracy: 0.7479\n",
      "Epoch 71/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5090 - accuracy: 0.7486\n",
      "Epoch 72/80\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5089 - accuracy: 0.7473\n",
      "Epoch 73/80\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5090 - accuracy: 0.7491\n",
      "Epoch 74/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5090 - accuracy: 0.7488\n",
      "Epoch 75/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5088 - accuracy: 0.7493\n",
      "Epoch 76/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5087 - accuracy: 0.7494\n",
      "Epoch 77/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5087 - accuracy: 0.7486\n",
      "Epoch 78/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5086 - accuracy: 0.7481\n",
      "Epoch 79/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5085 - accuracy: 0.7491\n",
      "Epoch 80/80\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5085 - accuracy: 0.7483\n"
     ]
    }
   ],
   "source": [
    "# Fit / train the keras model to the training data\n",
    "fit_model = nn_model.fit(X_train_scaled, y_train, epochs=80)\n",
    "#fit_model = nn_model.fit(X_train_scaled, y_train, epochs=100)\n",
    "#fit_model = nn_model.fit(X_train_scaled, y_train, epochs=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/1 - 0s - loss: 0.4899 - accuracy: 0.7399\n",
      "Loss: 0.5208412734794895, Accuracy: 0.7399417161941528\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model performance using the test data\n",
    "model_loss, model_accuracy = nn_model.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  }
 ],
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