AlphabetSoupChallenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Model design with binning of \"APPLICATION_TYPE\" and \"CLASSIFICATION\""
   ]
  },
  {
   "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>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": [
    "# Import our input dataset\n",
    "charity_df = pd.read_csv('Resources/charity_data.csv')\n",
    "charity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the \"EIN\" & \"NAME\" columns\n",
    "charity_cleaned_df = charity_df.drop(columns=[\"EIN\",\"NAME\"], axis=1)\n",
    "charity_cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE          object\n",
       "AFFILIATION               object\n",
       "CLASSIFICATION            object\n",
       "USE_CASE                  object\n",
       "ORGANIZATION              object\n",
       "STATUS                     int64\n",
       "INCOME_AMT                object\n",
       "SPECIAL_CONSIDERATIONS    object\n",
       "ASK_AMT                    int64\n",
       "IS_SUCCESSFUL              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charity_cleaned_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable list\n",
    "charity_cat = charity_cleaned_df.dtypes[charity_cleaned_df.dtypes == \"object\"].index.tolist()\n",
    "charity_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE          17\n",
       "AFFILIATION                6\n",
       "CLASSIFICATION            71\n",
       "USE_CASE                   5\n",
       "ORGANIZATION               4\n",
       "INCOME_AMT                 9\n",
       "SPECIAL_CONSIDERATIONS     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of unique values in each column\n",
    "charity_cleaned_df[charity_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T3     27037\n",
       "T4      1542\n",
       "T6      1216\n",
       "T5      1173\n",
       "T19     1065\n",
       "T8       737\n",
       "T7       725\n",
       "T10      528\n",
       "T9       156\n",
       "T13       66\n",
       "T12       27\n",
       "T2        16\n",
       "T25        3\n",
       "T14        3\n",
       "T29        2\n",
       "T15        2\n",
       "T17        1\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the APPLICATION_TYPE value counts\n",
    "application_counts = charity_cleaned_df.APPLICATION_TYPE.value_counts()\n",
    "application_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26fb0f31308>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the value counts\n",
    "application_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine which values to replace for bucketing\n",
    "replace_applications = list(application_counts[application_counts < 100].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace in DataFrame\n",
    "for application in replace_applications:\n",
    "    charity_cleaned_df.APPLICATION_TYPE = charity_cleaned_df.APPLICATION_TYPE.replace(application,\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "Other      120\n",
       "Name: APPLICATION_TYPE, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to make sure bucketing was successful\n",
    "charity_cleaned_df.APPLICATION_TYPE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "C2800       95\n",
       "C7100       75\n",
       "C1300       58\n",
       "C1280       50\n",
       "C1230       36\n",
       "C1400       34\n",
       "C2300       32\n",
       "C7200       32\n",
       "C1240       30\n",
       "C8000       20\n",
       "C7120       18\n",
       "C1500       16\n",
       "C6000       15\n",
       "C1800       15\n",
       "C1250       14\n",
       "C8200       11\n",
       "C1278       10\n",
       "C1238       10\n",
       "C1235        9\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the CLASSIFICATION value counts\n",
    "classification_counts = charity_cleaned_df.CLASSIFICATION.value_counts()\n",
    "classification_counts.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26fb1643d88>"
      ]
     },
     "execution_count": 13,
     "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\n",
    "classification_counts.plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine which values to replace for bucketing\n",
    "replace_classifications = list(classification_counts[classification_counts < 100].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace in DataFrame\n",
    "for classification in replace_classifications:\n",
    "    charity_cleaned_df.CLASSIFICATION = charity_cleaned_df.CLASSIFICATION.replace(classification,\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "Other      669\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to make sure bucketing was successful\n",
    "charity_cleaned_df.CLASSIFICATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</div>"
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       "    0    1    2    3    4    5    6    7    8    9   ...   38   39   40   41  \\\n",
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       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(charity_cleaned_df[charity_cat]))\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Add the encoded variable names to the DataFrame\n",
    "encode_df.columns = enc.get_feature_names(charity_cat)\n",
    "encode_df.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "   STATUS  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_Other  \\\n",
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       "3       1     6692              1                     0.0   \n",
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       "4                       0.0  \n",
       "\n",
       "[5 rows x 51 columns]"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "charity_cleaned_df = charity_cleaned_df.merge(encode_df,left_index=True, right_index=True)\n",
    "charity_cleaned_df = charity_cleaned_df.drop(charity_cat,1)\n",
    "charity_cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our target and features arrays\n",
    "y = charity_cleaned_df[\"IS_SUCCESSFUL\"].values\n",
    "X = charity_cleaned_df.drop([\"IS_SUCCESSFUL\"],1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00, 5.0000000e+03, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00],\n",
       "       [1.0000000e+00, 1.0859000e+05, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00],\n",
       "       [1.0000000e+00, 5.0000000e+03, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00],\n",
       "       ...,\n",
       "       [1.0000000e+00, 5.0000000e+03, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00],\n",
       "       [1.0000000e+00, 5.0000000e+03, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00],\n",
       "       [1.0000000e+00, 3.6500179e+07, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34299"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34299"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25724"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25724"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8575"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8575"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_scaled[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 10)                510       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 88        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 607\n",
      "Trainable params: 607\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 10\n",
    "hidden_nodes_layer2 = 8\n",
    "\n",
    "nn_1 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_1.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_1.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_1.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_1.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 4s 138us/sample - loss: 0.5914 - accuracy: 0.7133\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5633 - accuracy: 0.7276\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 2s 87us/sample - loss: 0.5585 - accuracy: 0.7298\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5561 - accuracy: 0.7305\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5545 - accuracy: 0.7313\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5529 - accuracy: 0.7329\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5520 - accuracy: 0.7324\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5507 - accuracy: 0.7336\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5494 - accuracy: 0.7335\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 2s 84us/sample - loss: 0.5490 - accuracy: 0.7338\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5482 - accuracy: 0.7342\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5483 - accuracy: 0.7335\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5463 - accuracy: 0.7338\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5460 - accuracy: 0.7348\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5458 - accuracy: 0.7353\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.5454 - accuracy: 0.7341\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5450 - accuracy: 0.7350\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5450 - accuracy: 0.7346\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5448 - accuracy: 0.7341\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.5443 - accuracy: 0.7352\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5439 - accuracy: 0.7352\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.5445 - accuracy: 0.7346\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5440 - accuracy: 0.7344\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5437 - accuracy: 0.7359\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5437 - accuracy: 0.7359\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5438 - accuracy: 0.7352\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5434 - accuracy: 0.7348\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 3s 105us/sample - loss: 0.5433 - accuracy: 0.7356\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 1s 58us/sample - loss: 0.5431 - accuracy: 0.7359\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5431 - accuracy: 0.7346\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 3s 106us/sample - loss: 0.5430 - accuracy: 0.7353\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5431 - accuracy: 0.7352\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 2s 81us/sample - loss: 0.5428 - accuracy: 0.7353\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5427 - accuracy: 0.7361\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5428 - accuracy: 0.7358\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5424 - accuracy: 0.7357\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5422 - accuracy: 0.7357\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5424 - accuracy: 0.7347\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5422 - accuracy: 0.7357\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.5424 - accuracy: 0.7362\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.5424 - accuracy: 0.7357\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5420 - accuracy: 0.7362\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5418 - accuracy: 0.7363\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.5420 - accuracy: 0.7365\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5417 - accuracy: 0.7371\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5417 - accuracy: 0.7368\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 1s 36us/sample - loss: 0.5416 - accuracy: 0.7369\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5413 - accuracy: 0.7372\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5418 - accuracy: 0.7377\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5415 - accuracy: 0.7355\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_1.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 1s - loss: 0.5532 - accuracy: 0.7272\n",
      "Loss: 0.5532226133277048, Accuracy: 0.7272303104400635\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_1.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": "markdown",
   "metadata": {},
   "source": [
    "# Model-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 10)                510       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 8)                 88        \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 6)                 54        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 7         \n",
      "=================================================================\n",
      "Total params: 659\n",
      "Trainable params: 659\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 10\n",
    "hidden_nodes_layer2 = 8\n",
    "hidden_nodes_layer3 = 6\n",
    "\n",
    "nn_2 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_2.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_2.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn_2.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_2.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_2.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.5890 - accuracy: 0.7091\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5572 - accuracy: 0.7271\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5530 - accuracy: 0.7287\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5511 - accuracy: 0.7296\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5499 - accuracy: 0.7308\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 1s 54us/sample - loss: 0.5486 - accuracy: 0.7316\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5486 - accuracy: 0.7321\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5474 - accuracy: 0.7320\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5471 - accuracy: 0.7326\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5467 - accuracy: 0.7331\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5464 - accuracy: 0.7327\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.5458 - accuracy: 0.7328\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5454 - accuracy: 0.7328\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5453 - accuracy: 0.7333\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5450 - accuracy: 0.7334\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5447 - accuracy: 0.7329\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5446 - accuracy: 0.7336\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5446 - accuracy: 0.7335\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5441 - accuracy: 0.7333\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5442 - accuracy: 0.7330\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 2s 78us/sample - loss: 0.5439 - accuracy: 0.7341\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5436 - accuracy: 0.7344s - loss: 0.5414 - ac\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5436 - accuracy: 0.7338\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5433 - accuracy: 0.7349\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5432 - accuracy: 0.7347\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5433 - accuracy: 0.7339\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5432 - accuracy: 0.7340\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5432 - accuracy: 0.7348\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5433 - accuracy: 0.7342\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5430 - accuracy: 0.7349\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5427 - accuracy: 0.7349\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5428 - accuracy: 0.7350\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5427 - accuracy: 0.7348\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5426 - accuracy: 0.7357\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5424 - accuracy: 0.7347\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5426 - accuracy: 0.7353\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5419 - accuracy: 0.7355s - loss: 0.5\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5423 - accuracy: 0.7350\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5419 - accuracy: 0.7352\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5421 - accuracy: 0.7354\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5418 - accuracy: 0.7354\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5418 - accuracy: 0.7363\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5421 - accuracy: 0.7358\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5416 - accuracy: 0.7358\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5420 - accuracy: 0.7356\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5417 - accuracy: 0.7367\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 3s 108us/sample - loss: 0.5416 - accuracy: 0.7360 - loss: 0.5403 - accu - ETA: 0s - loss: - ETA: 0s\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5414 - accuracy: 0.7368s - loss: 0.5412 \n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.5413 - accuracy: 0.7359\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 2s 91us/sample - loss: 0.5415 - accuracy: 0.7372\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_2.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5505 - accuracy: 0.7276\n",
      "Loss: 0.5504939567521432, Accuracy: 0.727580189704895\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_2.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_7 (Dense)              (None, 10)                510       \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 8)                 88        \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 8)                 72        \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 679\n",
      "Trainable params: 679\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 10\n",
    "hidden_nodes_layer2 = 8\n",
    "hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_3 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_3.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_3.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn_3.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_3.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_3.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.6103 - accuracy: 0.6862\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5666 - accuracy: 0.7190\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5595 - accuracy: 0.7261\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5554 - accuracy: 0.7292\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5526 - accuracy: 0.7291\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5515 - accuracy: 0.7299\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5499 - accuracy: 0.7313\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5490 - accuracy: 0.7312\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5484 - accuracy: 0.7315\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 2s 66us/sample - loss: 0.5477 - accuracy: 0.7327\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 78us/sample - loss: 0.5473 - accuracy: 0.7325\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5469 - accuracy: 0.7325\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5463 - accuracy: 0.7343\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5456 - accuracy: 0.7345\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.5453 - accuracy: 0.7348\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5452 - accuracy: 0.7351\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5453 - accuracy: 0.7347\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5446 - accuracy: 0.7353\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5449 - accuracy: 0.7362\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5448 - accuracy: 0.7341\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5444 - accuracy: 0.7364s - loss: 0.5424 - accuracy\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.5445 - accuracy: 0.7358\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5439 - accuracy: 0.7373\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5442 - accuracy: 0.7362\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5437 - accuracy: 0.7372\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5437 - accuracy: 0.7358\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5437 - accuracy: 0.7378\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5439 - accuracy: 0.7367\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.5432 - accuracy: 0.7374\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5431 - accuracy: 0.7350\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.5435 - accuracy: 0.7367\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5431 - accuracy: 0.7368\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5428 - accuracy: 0.7361s - loss: 0.543\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5433 - accuracy: 0.7369\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 2s 81us/sample - loss: 0.5429 - accuracy: 0.7367\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5428 - accuracy: 0.7369\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5424 - accuracy: 0.7371\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5423 - accuracy: 0.7379\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5424 - accuracy: 0.7372\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5424 - accuracy: 0.7368\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 2s 84us/sample - loss: 0.5424 - accuracy: 0.7374\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5419 - accuracy: 0.7362\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5418 - accuracy: 0.7371\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5419 - accuracy: 0.7373s - loss: 0.5\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5419 - accuracy: 0.7373\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5415 - accuracy: 0.7372\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5418 - accuracy: 0.7377\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5413 - accuracy: 0.7372\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5413 - accuracy: 0.7369\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.5410 - accuracy: 0.7383 - loss: 0.5436 - accuracy: 0.73 - ETA: 1s - l\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_3.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5512 - accuracy: 0.7258\n",
      "Loss: 0.5511872530400579, Accuracy: 0.7258309125900269\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_3.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/100\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.5413 - accuracy: 0.7366\n",
      "Epoch 2/100\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5415 - accuracy: 0.7376\n",
      "Epoch 3/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5408 - accuracy: 0.7376\n",
      "Epoch 4/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5415 - accuracy: 0.7374\n",
      "Epoch 5/100\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5411 - accuracy: 0.7376\n",
      "Epoch 6/100\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5413 - accuracy: 0.7372\n",
      "Epoch 7/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5410 - accuracy: 0.7379\n",
      "Epoch 8/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5406 - accuracy: 0.7371s - loss: 0.5428 - accuracy: 0.\n",
      "Epoch 9/100\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5411 - accuracy: 0.7372\n",
      "Epoch 10/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5415 - accuracy: 0.7371\n",
      "Epoch 11/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5405 - accuracy: 0.7374\n",
      "Epoch 12/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5405 - accuracy: 0.7368\n",
      "Epoch 13/100\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5406 - accuracy: 0.7379\n",
      "Epoch 14/100\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5408 - accuracy: 0.7372\n",
      "Epoch 15/100\n",
      "25724/25724 [==============================] - 2s 79us/sample - loss: 0.5406 - accuracy: 0.7378\n",
      "Epoch 16/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5407 - accuracy: 0.7376\n",
      "Epoch 17/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5407 - accuracy: 0.7367\n",
      "Epoch 18/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5405 - accuracy: 0.7375\n",
      "Epoch 19/100\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5405 - accuracy: 0.7372\n",
      "Epoch 20/100\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5404 - accuracy: 0.7385\n",
      "Epoch 21/100\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5402 - accuracy: 0.7385\n",
      "Epoch 22/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5407 - accuracy: 0.7377\n",
      "Epoch 23/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5405 - accuracy: 0.7374\n",
      "Epoch 24/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5405 - accuracy: 0.7372\n",
      "Epoch 25/100\n",
      "25724/25724 [==============================] - ETA: 0s - loss: 0.5399 - accuracy: 0.73 - 1s 57us/sample - loss: 0.5401 - accuracy: 0.7383\n",
      "Epoch 26/100\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5400 - accuracy: 0.7376\n",
      "Epoch 27/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5403 - accuracy: 0.7380\n",
      "Epoch 28/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5394 - accuracy: 0.7374\n",
      "Epoch 29/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5398 - accuracy: 0.7371\n",
      "Epoch 30/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5400 - accuracy: 0.7380\n",
      "Epoch 31/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5397 - accuracy: 0.7385\n",
      "Epoch 32/100\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5398 - accuracy: 0.7376\n",
      "Epoch 33/100\n",
      "25724/25724 [==============================] - 1s 56us/sample - loss: 0.5395 - accuracy: 0.7389\n",
      "Epoch 34/100\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5399 - accuracy: 0.7376\n",
      "Epoch 35/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5395 - accuracy: 0.7376\n",
      "Epoch 36/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5398 - accuracy: 0.7382\n",
      "Epoch 37/100\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5392 - accuracy: 0.7381\n",
      "Epoch 38/100\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5394 - accuracy: 0.7383\n",
      "Epoch 39/100\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5389 - accuracy: 0.7379\n",
      "Epoch 40/100\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5395 - accuracy: 0.7383\n",
      "Epoch 41/100\n",
      "25724/25724 [==============================] - 2s 63us/sample - loss: 0.5395 - accuracy: 0.7375\n",
      "Epoch 42/100\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5394 - accuracy: 0.7384\n",
      "Epoch 43/100\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5400 - accuracy: 0.7388\n",
      "Epoch 44/100\n",
      "25724/25724 [==============================] - 2s 78us/sample - loss: 0.5398 - accuracy: 0.7374\n",
      "Epoch 45/100\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5393 - accuracy: 0.7378\n",
      "Epoch 46/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5394 - accuracy: 0.7381\n",
      "Epoch 47/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5396 - accuracy: 0.7383\n",
      "Epoch 48/100\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5394 - accuracy: 0.7388\n",
      "Epoch 49/100\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5388 - accuracy: 0.7383\n",
      "Epoch 50/100\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5393 - accuracy: 0.7387\n",
      "Epoch 51/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5390 - accuracy: 0.7386\n",
      "Epoch 52/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5394 - accuracy: 0.7384\n",
      "Epoch 53/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5393 - accuracy: 0.7379\n",
      "Epoch 54/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5395 - accuracy: 0.7371\n",
      "Epoch 55/100\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5391 - accuracy: 0.7386\n",
      "Epoch 56/100\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.5391 - accuracy: 0.7379\n",
      "Epoch 57/100\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5390 - accuracy: 0.7386\n",
      "Epoch 58/100\n",
      "25724/25724 [==============================] - ETA: 0s - loss: 0.5381 - accuracy: 0.73 - 1s 39us/sample - loss: 0.5390 - accuracy: 0.7387\n",
      "Epoch 59/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5391 - accuracy: 0.7385\n",
      "Epoch 60/100\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5392 - accuracy: 0.7379\n",
      "Epoch 61/100\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5392 - accuracy: 0.7379\n",
      "Epoch 62/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5393 - accuracy: 0.7388s - loss: 0.5374 - accura\n",
      "Epoch 63/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5388 - accuracy: 0.7377\n",
      "Epoch 64/100\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5387 - accuracy: 0.7376\n",
      "Epoch 65/100\n",
      "25724/25724 [==============================] - 2s 63us/sample - loss: 0.5392 - accuracy: 0.7379\n",
      "Epoch 66/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5390 - accuracy: 0.7386\n",
      "Epoch 67/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5391 - accuracy: 0.7388\n",
      "Epoch 68/100\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5390 - accuracy: 0.7380\n",
      "Epoch 69/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5392 - accuracy: 0.7390\n",
      "Epoch 70/100\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5389 - accuracy: 0.7380\n",
      "Epoch 71/100\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5387 - accuracy: 0.7386\n",
      "Epoch 72/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5390 - accuracy: 0.7381\n",
      "Epoch 73/100\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5386 - accuracy: 0.7389\n",
      "Epoch 74/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25724/25724 [==============================] - 1s 56us/sample - loss: 0.5387 - accuracy: 0.7375\n",
      "Epoch 75/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5389 - accuracy: 0.7388\n",
      "Epoch 76/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5385 - accuracy: 0.7387\n",
      "Epoch 77/100\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5388 - accuracy: 0.7387\n",
      "Epoch 78/100\n",
      "25724/25724 [==============================] - 2s 84us/sample - loss: 0.5385 - accuracy: 0.7388\n",
      "Epoch 79/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5387 - accuracy: 0.7376s - loss: 0.5341 - \n",
      "Epoch 80/100\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5385 - accuracy: 0.7385\n",
      "Epoch 81/100\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.5385 - accuracy: 0.7383\n",
      "Epoch 82/100\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5385 - accuracy: 0.7384\n",
      "Epoch 83/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5380 - accuracy: 0.7396\n",
      "Epoch 84/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5382 - accuracy: 0.7385\n",
      "Epoch 85/100\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5384 - accuracy: 0.7388\n",
      "Epoch 86/100\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5383 - accuracy: 0.7390\n",
      "Epoch 87/100\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5386 - accuracy: 0.7388\n",
      "Epoch 88/100\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5383 - accuracy: 0.7386\n",
      "Epoch 89/100\n",
      "25724/25724 [==============================] - 2s 80us/sample - loss: 0.5383 - accuracy: 0.7381\n",
      "Epoch 90/100\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5385 - accuracy: 0.7387\n",
      "Epoch 91/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5379 - accuracy: 0.7384\n",
      "Epoch 92/100\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5384 - accuracy: 0.7396\n",
      "Epoch 93/100\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5378 - accuracy: 0.7388\n",
      "Epoch 94/100\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5376 - accuracy: 0.7388\n",
      "Epoch 95/100\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5378 - accuracy: 0.7385\n",
      "Epoch 96/100\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5382 - accuracy: 0.7389\n",
      "Epoch 97/100\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5378 - accuracy: 0.7390\n",
      "Epoch 98/100\n",
      "25724/25724 [==============================] - 2s 63us/sample - loss: 0.5380 - accuracy: 0.7391\n",
      "Epoch 99/100\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5377 - accuracy: 0.7397\n",
      "Epoch 100/100\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5378 - accuracy: 0.7383\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_3.fit(X_train_scaled,y_train,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5521 - accuracy: 0.7254\n",
      "Loss: 0.5520622838064811, Accuracy: 0.7253644466400146\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_3.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": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Note: Model design with binning of \"CLASSIFICATION\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our input dataset\n",
    "charity_df = pd.read_csv('Resources/charity_data.csv')\n",
    "charity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the \"EIN\" & \"NAME\" columns\n",
    "charity_cleaned_2 = charity_df.drop(columns=[\"EIN\",\"NAME\"], axis=1)\n",
    "charity_cleaned_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable list\n",
    "charity_cat_1 = charity_cleaned_2.dtypes[charity_cleaned_2.dtypes == \"object\"].index.tolist()\n",
    "charity_cat_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "APPLICATION_TYPE          17\n",
       "AFFILIATION                6\n",
       "CLASSIFICATION            71\n",
       "USE_CASE                   5\n",
       "ORGANIZATION               4\n",
       "INCOME_AMT                 9\n",
       "SPECIAL_CONSIDERATIONS     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of unique values in each column\n",
    "charity_cleaned_2[charity_cat_1].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "C2800       95\n",
       "C7100       75\n",
       "C1300       58\n",
       "C1280       50\n",
       "C1230       36\n",
       "C1400       34\n",
       "C2300       32\n",
       "C7200       32\n",
       "C1240       30\n",
       "C8000       20\n",
       "C7120       18\n",
       "C1500       16\n",
       "C6000       15\n",
       "C1800       15\n",
       "C1250       14\n",
       "C8200       11\n",
       "C1278       10\n",
       "C1238       10\n",
       "C1235        9\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the CLASSIFICATION value counts\n",
    "classification_counts_1 = charity_cleaned_2.CLASSIFICATION.value_counts()\n",
    "classification_counts_1.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine which values to replace for bucketing\n",
    "replace_classifications_1 = list(classification_counts_1[classification_counts_1 < 100].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace in DataFrame\n",
    "for classification in replace_classifications_1:\n",
    "    charity_cleaned_2.CLASSIFICATION = charity_cleaned_2.CLASSIFICATION.replace(classification,\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "Other      669\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to make sure bucketing was successful\n",
    "charity_cleaned_2.CLASSIFICATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the OneHotEncoder instance\n",
    "enc2 = OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_2 = pd.DataFrame(enc2.fit_transform(charity_cleaned_2[charity_cat_1]))\n",
    "encode_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
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    "# Add the encoded variable names to the DataFrame\n",
    "encode_2.columns = enc2.get_feature_names(charity_cat_1)\n",
    "encode_2.head()"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>5000</td>\n",
       "      <td>1</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>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>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>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>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>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>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>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>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 × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   STATUS  ASK_AMT  IS_SUCCESSFUL  APPLICATION_TYPE_T10  APPLICATION_TYPE_T12  \\\n",
       "0       1     5000              1                   1.0                   0.0   \n",
       "1       1   108590              1                   0.0                   0.0   \n",
       "2       1     5000              0                   0.0                   0.0   \n",
       "3       1     6692              1                   0.0                   0.0   \n",
       "4       1   142590              1                   0.0                   0.0   \n",
       "\n",
       "   APPLICATION_TYPE_T13  APPLICATION_TYPE_T14  APPLICATION_TYPE_T15  \\\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",
       "   APPLICATION_TYPE_T17  APPLICATION_TYPE_T19  ...  INCOME_AMT_1-9999  \\\n",
       "0                   0.0                   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  ...                0.0   \n",
       "4                   0.0                   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 58 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "charity_cleaned_3 = charity_cleaned_2.merge(encode_2,left_index=True, right_index=True)\n",
    "charity_cleaned_3 = charity_cleaned_3.drop(charity_cat_1,1)\n",
    "charity_cleaned_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our target and features arrays\n",
    "y = charity_cleaned_3[\"IS_SUCCESSFUL\"].values\n",
    "X = charity_cleaned_3.drop([\"IS_SUCCESSFUL\"],1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_scaled[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_11 (Dense)             (None, 12)                696       \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 12)                156       \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 13        \n",
      "=================================================================\n",
      "Total params: 865\n",
      "Trainable params: 865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 12\n",
    "hidden_nodes_layer2 = 12\n",
    "# hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_4 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_4.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_4.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# # Third hidden layer\n",
    "# nn_4.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_4.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_4.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5903 - accuracy: 0.7109\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5587 - accuracy: 0.7283\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5540 - accuracy: 0.7302\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 2s 76us/sample - loss: 0.5517 - accuracy: 0.7318\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5502 - accuracy: 0.7326\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5490 - accuracy: 0.7336\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5482 - accuracy: 0.7331\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5478 - accuracy: 0.7344\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.5469 - accuracy: 0.7340\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5466 - accuracy: 0.7333\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5459 - accuracy: 0.7350\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5457 - accuracy: 0.7352\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 1s 36us/sample - loss: 0.5453 - accuracy: 0.7344\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5450 - accuracy: 0.7350\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.5447 - accuracy: 0.7348\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5441 - accuracy: 0.7344\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5439 - accuracy: 0.7347\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5440 - accuracy: 0.7349\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5436 - accuracy: 0.7346\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5433 - accuracy: 0.7343\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5425 - accuracy: 0.7362\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5426 - accuracy: 0.7361\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5425 - accuracy: 0.7349\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5422 - accuracy: 0.7364\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5423 - accuracy: 0.7349\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 1s 36us/sample - loss: 0.5420 - accuracy: 0.7360\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 1s 36us/sample - loss: 0.5419 - accuracy: 0.7357\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5414 - accuracy: 0.7357\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5415 - accuracy: 0.7355\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5418 - accuracy: 0.7364\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5410 - accuracy: 0.7364\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5413 - accuracy: 0.7368\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5408 - accuracy: 0.7372\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5406 - accuracy: 0.7372\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5406 - accuracy: 0.7367\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 2s 81us/sample - loss: 0.5408 - accuracy: 0.7366\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5399 - accuracy: 0.7367\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5405 - accuracy: 0.7362\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5402 - accuracy: 0.7364\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5403 - accuracy: 0.7368\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5403 - accuracy: 0.7365\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5400 - accuracy: 0.7364\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 1s 54us/sample - loss: 0.5397 - accuracy: 0.7371\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5400 - accuracy: 0.7373\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5400 - accuracy: 0.7361\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5398 - accuracy: 0.7367\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5397 - accuracy: 0.7370\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5399 - accuracy: 0.7375\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5398 - accuracy: 0.7371\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.5395 - accuracy: 0.7365\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_4.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5508 - accuracy: 0.7266\n",
      "Loss: 0.5507525352277839, Accuracy: 0.7266472578048706\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_4.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_14 (Dense)             (None, 12)                696       \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 10)                130       \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 8)                 88        \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 923\n",
      "Trainable params: 923\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 12\n",
    "hidden_nodes_layer2 = 10\n",
    "hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_5 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_5.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_5.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn_5.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_5.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_5.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/75\n",
      "25724/25724 [==============================] - 1s 58us/sample - loss: 0.6028 - accuracy: 0.6948\n",
      "Epoch 2/75\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5604 - accuracy: 0.7257\n",
      "Epoch 3/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5544 - accuracy: 0.7284\n",
      "Epoch 4/75\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5512 - accuracy: 0.7316\n",
      "Epoch 5/75\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5493 - accuracy: 0.7322\n",
      "Epoch 6/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5482 - accuracy: 0.7311\n",
      "Epoch 7/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5476 - accuracy: 0.7329\n",
      "Epoch 8/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5465 - accuracy: 0.7330\n",
      "Epoch 9/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5466 - accuracy: 0.7333\n",
      "Epoch 10/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5461 - accuracy: 0.7335\n",
      "Epoch 11/75\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5453 - accuracy: 0.7337\n",
      "Epoch 12/75\n",
      "25724/25724 [==============================] - 3s 105us/sample - loss: 0.5451 - accuracy: 0.7345\n",
      "Epoch 13/75\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5443 - accuracy: 0.7344\n",
      "Epoch 14/75\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5445 - accuracy: 0.7351\n",
      "Epoch 15/75\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.5440 - accuracy: 0.7339 - loss: 0.5401 \n",
      "Epoch 16/75\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5432 - accuracy: 0.7349\n",
      "Epoch 17/75\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5435 - accuracy: 0.7344\n",
      "Epoch 18/75\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5429 - accuracy: 0.7365\n",
      "Epoch 19/75\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.5431 - accuracy: 0.7348\n",
      "Epoch 20/75\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5430 - accuracy: 0.7353\n",
      "Epoch 21/75\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5427 - accuracy: 0.7360\n",
      "Epoch 22/75\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5423 - accuracy: 0.7355\n",
      "Epoch 23/75\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.5420 - accuracy: 0.7360\n",
      "Epoch 24/75\n",
      "25724/25724 [==============================] - 2s 91us/sample - loss: 0.5421 - accuracy: 0.7364\n",
      "Epoch 25/75\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5421 - accuracy: 0.7354\n",
      "Epoch 26/75\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5417 - accuracy: 0.7363\n",
      "Epoch 27/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5416 - accuracy: 0.7364\n",
      "Epoch 28/75\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5413 - accuracy: 0.7367\n",
      "Epoch 29/75\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5412 - accuracy: 0.7365\n",
      "Epoch 30/75\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.5408 - accuracy: 0.7372\n",
      "Epoch 31/75\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5412 - accuracy: 0.7365\n",
      "Epoch 32/75\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.5409 - accuracy: 0.7372\n",
      "Epoch 33/75\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5410 - accuracy: 0.7367\n",
      "Epoch 34/75\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5406 - accuracy: 0.7368\n",
      "Epoch 35/75\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5404 - accuracy: 0.7367\n",
      "Epoch 36/75\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5401 - accuracy: 0.7372\n",
      "Epoch 37/75\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5406 - accuracy: 0.7372\n",
      "Epoch 38/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5403 - accuracy: 0.7380\n",
      "Epoch 39/75\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5401 - accuracy: 0.7377\n",
      "Epoch 40/75\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5403 - accuracy: 0.7372\n",
      "Epoch 41/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5399 - accuracy: 0.7364\n",
      "Epoch 42/75\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5401 - accuracy: 0.7379\n",
      "Epoch 43/75\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.5396 - accuracy: 0.7385\n",
      "Epoch 44/75\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5397 - accuracy: 0.7363\n",
      "Epoch 45/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5397 - accuracy: 0.7376\n",
      "Epoch 46/75\n",
      "25724/25724 [==============================] - 1s 49us/sample - loss: 0.5393 - accuracy: 0.7370\n",
      "Epoch 47/75\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5391 - accuracy: 0.7393\n",
      "Epoch 48/75\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5395 - accuracy: 0.7375\n",
      "Epoch 49/75\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.5393 - accuracy: 0.7382\n",
      "Epoch 50/75\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5391 - accuracy: 0.7376\n",
      "Epoch 51/75\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5393 - accuracy: 0.7379\n",
      "Epoch 52/75\n",
      "25724/25724 [==============================] - 1s 56us/sample - loss: 0.5391 - accuracy: 0.7371\n",
      "Epoch 53/75\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.5389 - accuracy: 0.7385\n",
      "Epoch 54/75\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5390 - accuracy: 0.7383\n",
      "Epoch 55/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5388 - accuracy: 0.7389\n",
      "Epoch 56/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5391 - accuracy: 0.7369\n",
      "Epoch 57/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5385 - accuracy: 0.7386\n",
      "Epoch 58/75\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.5389 - accuracy: 0.7380\n",
      "Epoch 59/75\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5387 - accuracy: 0.7371\n",
      "Epoch 60/75\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.5381 - accuracy: 0.7386\n",
      "Epoch 61/75\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.5385 - accuracy: 0.7386\n",
      "Epoch 62/75\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5381 - accuracy: 0.7386\n",
      "Epoch 63/75\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5384 - accuracy: 0.7385\n",
      "Epoch 64/75\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.5383 - accuracy: 0.7374\n",
      "Epoch 65/75\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5384 - accuracy: 0.7386\n",
      "Epoch 66/75\n",
      "25724/25724 [==============================] - 2s 81us/sample - loss: 0.5383 - accuracy: 0.7379\n",
      "Epoch 67/75\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5381 - accuracy: 0.7391\n",
      "Epoch 68/75\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5383 - accuracy: 0.7381\n",
      "Epoch 69/75\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.5382 - accuracy: 0.7383\n",
      "Epoch 70/75\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.5382 - accuracy: 0.7390\n",
      "Epoch 71/75\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5385 - accuracy: 0.7374\n",
      "Epoch 72/75\n",
      "25724/25724 [==============================] - 2s 91us/sample - loss: 0.5385 - accuracy: 0.7385\n",
      "Epoch 73/75\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5383 - accuracy: 0.7384\n",
      "Epoch 74/75\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.5380 - accuracy: 0.7382\n",
      "Epoch 75/75\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5379 - accuracy: 0.7378\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_5.fit(X_train_scaled,y_train,epochs=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5537 - accuracy: 0.7259\n",
      "Loss: 0.5537094553218986, Accuracy: 0.7259474992752075\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_5.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_18 (Dense)             (None, 12)                696       \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 10)                130       \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 8)                 88        \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 923\n",
      "Trainable params: 923\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 12\n",
    "hidden_nodes_layer2 = 10\n",
    "hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_6 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_6.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_6.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn_6.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_6.add(tf.keras.layers.Dense(units=1, activation=\"tanh\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_6.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_6.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/75\n",
      "25724/25724 [==============================] - 2s 88us/sample - loss: 0.6545 - accuracy: 0.7052s - loss: 0.6769 - accura\n",
      "Epoch 2/75\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5773 - accuracy: 0.7238\n",
      "Epoch 3/75\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5648 - accuracy: 0.7274\n",
      "Epoch 4/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5593 - accuracy: 0.7288\n",
      "Epoch 5/75\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5585 - accuracy: 0.7286\n",
      "Epoch 6/75\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5567 - accuracy: 0.7292\n",
      "Epoch 7/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5556 - accuracy: 0.7297\n",
      "Epoch 8/75\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5545 - accuracy: 0.7314\n",
      "Epoch 9/75\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5561 - accuracy: 0.7304\n",
      "Epoch 10/75\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5539 - accuracy: 0.7318\n",
      "Epoch 11/75\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5515 - accuracy: 0.7304\n",
      "Epoch 12/75\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5522 - accuracy: 0.7313\n",
      "Epoch 13/75\n",
      "25724/25724 [==============================] - 1s 54us/sample - loss: 0.5523 - accuracy: 0.7320\n",
      "Epoch 14/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5488 - accuracy: 0.7313\n",
      "Epoch 15/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5494 - accuracy: 0.7315\n",
      "Epoch 16/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5486 - accuracy: 0.7324\n",
      "Epoch 17/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5492 - accuracy: 0.7319\n",
      "Epoch 18/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5487 - accuracy: 0.7321\n",
      "Epoch 19/75\n",
      "25724/25724 [==============================] - 3s 104us/sample - loss: 0.5488 - accuracy: 0.7322\n",
      "Epoch 20/75\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5504 - accuracy: 0.7311\n",
      "Epoch 21/75\n",
      "25724/25724 [==============================] - 2s 66us/sample - loss: 0.5490 - accuracy: 0.7331\n",
      "Epoch 22/75\n",
      "25724/25724 [==============================] - 2s 66us/sample - loss: 0.5472 - accuracy: 0.7326\n",
      "Epoch 23/75\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.5475 - accuracy: 0.7328\n",
      "Epoch 24/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5467 - accuracy: 0.7343\n",
      "Epoch 25/75\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5473 - accuracy: 0.7338\n",
      "Epoch 26/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5477 - accuracy: 0.7342\n",
      "Epoch 27/75\n",
      "25724/25724 [==============================] - ETA: 0s - loss: 0.5473 - accuracy: 0.73 - 2s 94us/sample - loss: 0.5472 - accuracy: 0.7336\n",
      "Epoch 28/75\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5486 - accuracy: 0.7339\n",
      "Epoch 29/75\n",
      "25724/25724 [==============================] - 1s 37us/sample - loss: 0.5475 - accuracy: 0.7334\n",
      "Epoch 30/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5451 - accuracy: 0.7339\n",
      "Epoch 31/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5467 - accuracy: 0.7348\n",
      "Epoch 32/75\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5481 - accuracy: 0.7332\n",
      "Epoch 33/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5455 - accuracy: 0.7355\n",
      "Epoch 34/75\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5458 - accuracy: 0.7340\n",
      "Epoch 35/75\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5452 - accuracy: 0.7350\n",
      "Epoch 36/75\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5446 - accuracy: 0.7351\n",
      "Epoch 37/75\n",
      "25724/25724 [==============================] - 1s 56us/sample - loss: 0.5449 - accuracy: 0.7352\n",
      "Epoch 38/75\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.5473 - accuracy: 0.7353\n",
      "Epoch 39/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5448 - accuracy: 0.7357\n",
      "Epoch 40/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5447 - accuracy: 0.7350\n",
      "Epoch 41/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5448 - accuracy: 0.7337\n",
      "Epoch 42/75\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5448 - accuracy: 0.7343\n",
      "Epoch 43/75\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5440 - accuracy: 0.7342\n",
      "Epoch 44/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5455 - accuracy: 0.7343\n",
      "Epoch 45/75\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.5440 - accuracy: 0.7358\n",
      "Epoch 46/75\n",
      "25724/25724 [==============================] - 1s 49us/sample - loss: 0.5482 - accuracy: 0.7364\n",
      "Epoch 47/75\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5479 - accuracy: 0.7357\n",
      "Epoch 48/75\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5471 - accuracy: 0.7368\n",
      "Epoch 49/75\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.5477 - accuracy: 0.7349\n",
      "Epoch 50/75\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5449 - accuracy: 0.7352\n",
      "Epoch 51/75\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5438 - accuracy: 0.7355\n",
      "Epoch 52/75\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.5448 - accuracy: 0.7360\n",
      "Epoch 53/75\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5438 - accuracy: 0.7358\n",
      "Epoch 54/75\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5428 - accuracy: 0.7349\n",
      "Epoch 55/75\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5429 - accuracy: 0.7358\n",
      "Epoch 56/75\n",
      "25724/25724 [==============================] - 2s 88us/sample - loss: 0.5434 - accuracy: 0.7356\n",
      "Epoch 57/75\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5432 - accuracy: 0.7361\n",
      "Epoch 58/75\n",
      "25724/25724 [==============================] - 4s 151us/sample - loss: 0.5427 - accuracy: 0.7357\n",
      "Epoch 59/75\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5432 - accuracy: 0.7365\n",
      "Epoch 60/75\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5431 - accuracy: 0.7360\n",
      "Epoch 61/75\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.5427 - accuracy: 0.7347\n",
      "Epoch 62/75\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5443 - accuracy: 0.7352\n",
      "Epoch 63/75\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5431 - accuracy: 0.7349\n",
      "Epoch 64/75\n",
      "25724/25724 [==============================] - 1s 38us/sample - loss: 0.5434 - accuracy: 0.7352\n",
      "Epoch 65/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5433 - accuracy: 0.7356\n",
      "Epoch 66/75\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5462 - accuracy: 0.7357\n",
      "Epoch 67/75\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5430 - accuracy: 0.7342\n",
      "Epoch 68/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5434 - accuracy: 0.7366\n",
      "Epoch 69/75\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5446 - accuracy: 0.7347\n",
      "Epoch 70/75\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.5434 - accuracy: 0.7360\n",
      "Epoch 71/75\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5447 - accuracy: 0.7365\n",
      "Epoch 72/75\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5443 - accuracy: 0.7370\n",
      "Epoch 73/75\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5436 - accuracy: 0.7357\n",
      "Epoch 74/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5429 - accuracy: 0.7363\n",
      "Epoch 75/75\n",
      "25724/25724 [==============================] - 1s 39us/sample - loss: 0.5451 - accuracy: 0.7358\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_6.fit(X_train_scaled,y_train,epochs=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5545 - accuracy: 0.7261\n",
      "Loss: 0.5545304873733409, Accuracy: 0.726064145565033\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_6.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_22 (Dense)             (None, 24)                1392      \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 12)                300       \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 8)                 104       \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 1,805\n",
      "Trainable params: 1,805\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 24\n",
    "hidden_nodes_layer2 = 12\n",
    "hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_7 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_7.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_7.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn_7.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_7.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_7.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_7.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5824 - accuracy: 0.7126\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5563 - accuracy: 0.7284\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5512 - accuracy: 0.7315\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5486 - accuracy: 0.7327\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5474 - accuracy: 0.7327\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5457 - accuracy: 0.7331\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5450 - accuracy: 0.7348\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5450 - accuracy: 0.7343\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5444 - accuracy: 0.7345\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5433 - accuracy: 0.7349\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.5429 - accuracy: 0.7351\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5425 - accuracy: 0.7362\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5423 - accuracy: 0.7355\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.5422 - accuracy: 0.7359\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5414 - accuracy: 0.7365\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.5414 - accuracy: 0.7364\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5410 - accuracy: 0.7369\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 1s 49us/sample - loss: 0.5413 - accuracy: 0.7369\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 3s 102us/sample - loss: 0.5405 - accuracy: 0.7365\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5403 - accuracy: 0.7377\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5404 - accuracy: 0.7375\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 1s 51us/sample - loss: 0.5402 - accuracy: 0.7379\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.5397 - accuracy: 0.7385\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5400 - accuracy: 0.7369\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5394 - accuracy: 0.7372\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 1s 46us/sample - loss: 0.5392 - accuracy: 0.7378\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5393 - accuracy: 0.7370\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 1s 47us/sample - loss: 0.5390 - accuracy: 0.7372\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5384 - accuracy: 0.7374\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5385 - accuracy: 0.7373\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5390 - accuracy: 0.7380\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5380 - accuracy: 0.7382\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5381 - accuracy: 0.7378\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5380 - accuracy: 0.7379\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 1s 52us/sample - loss: 0.5380 - accuracy: 0.7379\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5378 - accuracy: 0.7379\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 2s 61us/sample - loss: 0.5378 - accuracy: 0.7383\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 1s 50us/sample - loss: 0.5379 - accuracy: 0.7384\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 1s 56us/sample - loss: 0.5375 - accuracy: 0.7380\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5377 - accuracy: 0.7385\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5377 - accuracy: 0.7382\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 1s 56us/sample - loss: 0.5373 - accuracy: 0.7383\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 1s 53us/sample - loss: 0.5369 - accuracy: 0.7386\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.5373 - accuracy: 0.7384\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.5372 - accuracy: 0.7382\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5373 - accuracy: 0.7386\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5369 - accuracy: 0.7379\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 1s 55us/sample - loss: 0.5370 - accuracy: 0.7393\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 2s 59us/sample - loss: 0.5367 - accuracy: 0.7388\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 1s 48us/sample - loss: 0.5369 - accuracy: 0.7390\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_7.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5506 - accuracy: 0.7272\n",
      "Loss: 0.5505541829882141, Accuracy: 0.7272303104400635\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_7.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_35 (Dense)             (None, 24)                1392      \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 12)                300       \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 1)                 13        \n",
      "=================================================================\n",
      "Total params: 1,705\n",
      "Trainable params: 1,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 24\n",
    "hidden_nodes_layer2 = 12\n",
    "# hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_8 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_8.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_8.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# # Third hidden layer\n",
    "# nn_8.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_8.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_8.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_8.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5939 - accuracy: 0.7087\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5580 - accuracy: 0.7293\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5519 - accuracy: 0.7297\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5499 - accuracy: 0.7308\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5486 - accuracy: 0.7313\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 2s 84us/sample - loss: 0.5474 - accuracy: 0.7332\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5468 - accuracy: 0.7326\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5457 - accuracy: 0.7327\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5453 - accuracy: 0.7339\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 1s 44us/sample - loss: 0.5446 - accuracy: 0.7349\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5438 - accuracy: 0.7351\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 1s 54us/sample - loss: 0.5438 - accuracy: 0.7355\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 2s 76us/sample - loss: 0.5432 - accuracy: 0.7350\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5429 - accuracy: 0.7352\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5421 - accuracy: 0.7353\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.5417 - accuracy: 0.7362\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 2s 84us/sample - loss: 0.5422 - accuracy: 0.7360\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 1s 49us/sample - loss: 0.5415 - accuracy: 0.7370\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5414 - accuracy: 0.7361\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 2s 79us/sample - loss: 0.5408 - accuracy: 0.7356\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5407 - accuracy: 0.7367\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5407 - accuracy: 0.7350\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.5407 - accuracy: 0.7367\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5403 - accuracy: 0.7353\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5402 - accuracy: 0.7371\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.5398 - accuracy: 0.7373\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 1s 45us/sample - loss: 0.5397 - accuracy: 0.7375\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5401 - accuracy: 0.7360\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.5398 - accuracy: 0.7359\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 1s 58us/sample - loss: 0.5392 - accuracy: 0.7374\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.5395 - accuracy: 0.7368\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5391 - accuracy: 0.7377\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5391 - accuracy: 0.7387\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5388 - accuracy: 0.7374\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5388 - accuracy: 0.7382\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5389 - accuracy: 0.7377\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5386 - accuracy: 0.7371\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5387 - accuracy: 0.7391\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.5383 - accuracy: 0.7372\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 2s 69us/sample - loss: 0.5384 - accuracy: 0.7375\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.5381 - accuracy: 0.7388\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 1s 43us/sample - loss: 0.5382 - accuracy: 0.7377\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 2s 79us/sample - loss: 0.5379 - accuracy: 0.7385\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 1s 42us/sample - loss: 0.5379 - accuracy: 0.7387\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.5381 - accuracy: 0.7387\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 2s 62us/sample - loss: 0.5378 - accuracy: 0.7386\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 1s 40us/sample - loss: 0.5378 - accuracy: 0.7399\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 1s 41us/sample - loss: 0.5376 - accuracy: 0.7388\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 2s 88us/sample - loss: 0.5374 - accuracy: 0.7387\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 1s 57us/sample - loss: 0.5372 - accuracy: 0.7377\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_8.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5538 - accuracy: 0.7250\n",
      "Loss: 0.5537756930217799, Accuracy: 0.7250145673751831\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_8.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": "markdown",
   "metadata": {},
   "source": [
    "Note: Model design with binning of \"CLASSIFICATION\" and \"NAME\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
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       "    <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": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our input dataset\n",
    "charity_df = pd.read_csv('Resources/charity_data.csv')\n",
    "charity_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EIN                        int64\n",
       "NAME                      object\n",
       "APPLICATION_TYPE          object\n",
       "AFFILIATION               object\n",
       "CLASSIFICATION            object\n",
       "USE_CASE                  object\n",
       "ORGANIZATION              object\n",
       "STATUS                     int64\n",
       "INCOME_AMT                object\n",
       "SPECIAL_CONSIDERATIONS    object\n",
       "ASK_AMT                    int64\n",
       "IS_SUCCESSFUL              int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charity_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charity_3 = charity_df.copy()\n",
    "charity_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NAME',\n",
       " 'APPLICATION_TYPE',\n",
       " 'AFFILIATION',\n",
       " 'CLASSIFICATION',\n",
       " 'USE_CASE',\n",
       " 'ORGANIZATION',\n",
       " 'INCOME_AMT',\n",
       " 'SPECIAL_CONSIDERATIONS']"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable list\n",
    "charity_cat_3 = charity_3.dtypes[charity_3.dtypes == \"object\"].index.tolist()\n",
    "charity_cat_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of unique values in each column\n",
    "charity_3[charity_cat_3].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "C2800       95\n",
       "C7100       75\n",
       "C1300       58\n",
       "C1280       50\n",
       "C1230       36\n",
       "C1400       34\n",
       "C2300       32\n",
       "C7200       32\n",
       "C1240       30\n",
       "C8000       20\n",
       "C7120       18\n",
       "C1500       16\n",
       "C6000       15\n",
       "C1800       15\n",
       "C1250       14\n",
       "C8200       11\n",
       "C1278       10\n",
       "C1238       10\n",
       "C1235        9\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the CLASSIFICATION value counts\n",
    "classification_counts_3 = charity_3.CLASSIFICATION.value_counts()\n",
    "classification_counts_3.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine which values to replace for bucketing\n",
    "replace_classifications_3 = list(classification_counts_3[classification_counts_3 < 100].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace in DataFrame\n",
    "for classification in replace_classifications_3:\n",
    "    charity_3.CLASSIFICATION = charity_3.CLASSIFICATION.replace(classification,\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C1000    17326\n",
       "C2000     6074\n",
       "C1200     4837\n",
       "C3000     1918\n",
       "C2100     1883\n",
       "C7000      777\n",
       "Other      669\n",
       "C1700      287\n",
       "C4000      194\n",
       "C5000      116\n",
       "C1270      114\n",
       "C2700      104\n",
       "Name: CLASSIFICATION, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to make sure bucketing was successful\n",
    "charity_3.CLASSIFICATION.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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",
       "PTA TEXAS CONGRESS                                                     368\n",
       "SOROPTIMIST INTERNATIONAL OF THE AMERICAS INC                          331\n",
       "ALPHA PHI SIGMA                                                        313\n",
       "TOASTMASTERS INTERNATIONAL                                             293\n",
       "MOST WORSHIPFUL STRINGER FREE AND ACCEPTED MASONS                      287\n",
       "LITTLE LEAGUE BASEBALL INC                                             277\n",
       "INTERNATIONAL ASSOCIATION OF LIONS CLUBS                               266\n",
       "MOMS CLUB                                                              210\n",
       "INTERNATIONAL ASSOCIATION OF SHEET METAL AIR RAIL & TRANSPORTATION     206\n",
       "AMERICAN ASSOCIATION OF UNIVERSITY WOMEN                               197\n",
       "FARMERS EDUCATIONAL AND COOPERATIVE UNION OF AMERICA                   166\n",
       "KNIGHTS OF COLUMBUS                                                    158\n",
       "HABITAT FOR HUMANITY INTERNATIONAL INC                                 154\n",
       "TENNESSEE ORDER OF THE EASTERN STAR                                    151\n",
       "VETERANS OF FOREIGN WARS OF THE UNITED STATES AUXILIARY                144\n",
       "PTA UTAH CONGRESS                                                      140\n",
       "THE UNITED STATES PONY CLUBS INC                                       136\n",
       "CIVITAN INTERNATIONAL                                                  131\n",
       "SIGMA BETA DELTA INC                                                   127\n",
       "MONTANA 4-H FOUNDATION INC                                             107\n",
       "HONOR SOCIETY OF PHI KAPPA PHI                                         107\n",
       "WASHINGTON STATE GRANGE                                                106\n",
       "UNIVERSITY OF WYOMING                                                  105\n",
       "DEMOLAY INTERNATIONAL                                                  104\n",
       "SERTOMA INC                                                            103\n",
       "AIR FORCE ASSOCIATION                                                   99\n",
       "WORKERS UNITED                                                          97\n",
       "GAMMA THETA UPSILON                                                     92\n",
       "INTERNATIONAL ASSOCIATION OF FIRE FIGHTERS                              91\n",
       "SOCIETY OF SAINT VINCENT DE PAUL COUNCIL OF LOS ANGELES                 87\n",
       "Name: NAME, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the NAME value counts\n",
    "name_counts_3 = charity_3.NAME.value_counts()\n",
    "name_counts_3.head(35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine which values to replace for bucketing\n",
    "replace_names_3 = list(name_counts_3[name_counts_3 < 100].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace in DataFrame\n",
    "for name in replace_names_3:\n",
    "    charity_3.NAME = charity_3.NAME.replace(name,\"Other\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other                                                                 25987\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",
       "TOASTMASTERS INTERNATIONAL                                              293\n",
       "MOST WORSHIPFUL STRINGER FREE AND ACCEPTED MASONS                       287\n",
       "LITTLE LEAGUE BASEBALL INC                                              277\n",
       "INTERNATIONAL ASSOCIATION OF LIONS CLUBS                                266\n",
       "MOMS CLUB                                                               210\n",
       "INTERNATIONAL ASSOCIATION OF SHEET METAL AIR RAIL & TRANSPORTATION      206\n",
       "AMERICAN ASSOCIATION OF UNIVERSITY WOMEN                                197\n",
       "FARMERS EDUCATIONAL AND COOPERATIVE UNION OF AMERICA                    166\n",
       "KNIGHTS OF COLUMBUS                                                     158\n",
       "HABITAT FOR HUMANITY INTERNATIONAL INC                                  154\n",
       "TENNESSEE ORDER OF THE EASTERN STAR                                     151\n",
       "VETERANS OF FOREIGN WARS OF THE UNITED STATES AUXILIARY                 144\n",
       "PTA UTAH CONGRESS                                                       140\n",
       "THE UNITED STATES PONY CLUBS INC                                        136\n",
       "CIVITAN INTERNATIONAL                                                   131\n",
       "SIGMA BETA DELTA INC                                                    127\n",
       "MONTANA 4-H FOUNDATION INC                                              107\n",
       "HONOR SOCIETY OF PHI KAPPA PHI                                          107\n",
       "WASHINGTON STATE GRANGE                                                 106\n",
       "UNIVERSITY OF WYOMING                                                   105\n",
       "DEMOLAY INTERNATIONAL                                                   104\n",
       "SERTOMA INC                                                             103\n",
       "Name: NAME, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to make sure bucketing was successful\n",
    "charity_3.NAME.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the OneHotEncoder instance\n",
    "enc3 = OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_3 = pd.DataFrame(enc3.fit_transform(charity_3[charity_cat_3]))\n",
    "encode_3.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 201,
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     "execution_count": 201,
     "metadata": {},
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   "source": [
    "# Add the encoded variable names to the DataFrame\n",
    "encode_3.columns = enc3.get_feature_names(charity_cat_3)\n",
    "encode_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
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       "      <th>SPECIAL_CONSIDERATIONS_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10520599</td>\n",
       "      <td>1</td>\n",
       "      <td>5000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10531628</td>\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>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>10547893</td>\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>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>10553066</td>\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>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>10556103</td>\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>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 × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        EIN  STATUS  ASK_AMT  IS_SUCCESSFUL  NAME_ALPHA PHI SIGMA  \\\n",
       "0  10520599       1     5000              1                   0.0   \n",
       "1  10531628       1   108590              1                   0.0   \n",
       "2  10547893       1     5000              0                   0.0   \n",
       "3  10553066       1     6692              1                   0.0   \n",
       "4  10556103       1   142590              1                   0.0   \n",
       "\n",
       "   NAME_AMATEUR ATHLETIC UNION OF THE UNITED STATES INC  \\\n",
       "0                                                0.0      \n",
       "1                                                0.0      \n",
       "2                                                0.0      \n",
       "3                                                0.0      \n",
       "4                                                0.0      \n",
       "\n",
       "   NAME_AMERICAN ASSOCIATION OF UNIVERSITY WOMEN  NAME_CIVITAN INTERNATIONAL  \\\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_DEMOLAY INTERNATIONAL  \\\n",
       "0                         0.0   \n",
       "1                         0.0   \n",
       "2                         0.0   \n",
       "3                         0.0   \n",
       "4                         0.0   \n",
       "\n",
       "   NAME_FARMERS EDUCATIONAL AND COOPERATIVE UNION OF AMERICA  ...  \\\n",
       "0                                                0.0          ...   \n",
       "1                                                0.0          ...   \n",
       "2                                                0.0          ...   \n",
       "3                                                0.0          ...   \n",
       "4                                                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 90 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "charity_enc_3 = charity_3.merge(encode_3,left_index=True, right_index=True)\n",
    "charity_enc_3 = charity_enc_3.drop(charity_cat_3,1)\n",
    "charity_enc_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our target and features arrays\n",
    "y = charity_enc_3[\"IS_SUCCESSFUL\"].values\n",
    "X = charity_enc_3.drop([\"IS_SUCCESSFUL\"],1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_scaled[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_11\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_38 (Dense)             (None, 24)                2160      \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 12)                300       \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 1)                 13        \n",
      "=================================================================\n",
      "Total params: 2,473\n",
      "Trainable params: 2,473\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 24\n",
    "hidden_nodes_layer2 = 12\n",
    "\n",
    "nn_9 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_9.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_9.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_9.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_9.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_9.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.5301 - accuracy: 0.7327\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 2s 64us/sample - loss: 0.4925 - accuracy: 0.7561\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 3s 97us/sample - loss: 0.4877 - accuracy: 0.7594\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 3s 132us/sample - loss: 0.4854 - accuracy: 0.7596\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.4834 - accuracy: 0.7596\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 3s 128us/sample - loss: 0.4820 - accuracy: 0.7615\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.4811 - accuracy: 0.7621\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 2s 80us/sample - loss: 0.4804 - accuracy: 0.7616\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 3s 122us/sample - loss: 0.4798 - accuracy: 0.7633\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 3s 133us/sample - loss: 0.4793 - accuracy: 0.7622\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.4785 - accuracy: 0.7633\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4783 - accuracy: 0.7632\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 3s 106us/sample - loss: 0.4777 - accuracy: 0.7630\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4774 - accuracy: 0.7639\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4772 - accuracy: 0.7622\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 91us/sample - loss: 0.4768 - accuracy: 0.7636\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 3s 114us/sample - loss: 0.4762 - accuracy: 0.7656\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.4758 - accuracy: 0.7650\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4758 - accuracy: 0.7659\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 2s 87us/sample - loss: 0.4754 - accuracy: 0.7659\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.4746 - accuracy: 0.7657\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.4749 - accuracy: 0.7645\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4742 - accuracy: 0.7651\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.4743 - accuracy: 0.7652\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4741 - accuracy: 0.7651\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 2s 84us/sample - loss: 0.4738 - accuracy: 0.7667\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.4734 - accuracy: 0.7657\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 2s 87us/sample - loss: 0.4732 - accuracy: 0.7653\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4728 - accuracy: 0.7666\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4726 - accuracy: 0.7684\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.4722 - accuracy: 0.7686\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4723 - accuracy: 0.7671\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.4719 - accuracy: 0.7676\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4717 - accuracy: 0.7669\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4715 - accuracy: 0.7666\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4712 - accuracy: 0.7673\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4709 - accuracy: 0.7664\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4710 - accuracy: 0.7678\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 2s 87us/sample - loss: 0.4708 - accuracy: 0.7683\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.4702 - accuracy: 0.7680\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.4708 - accuracy: 0.7678\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.4701 - accuracy: 0.7682\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4701 - accuracy: 0.7690\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4704 - accuracy: 0.7687\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - ETA: 0s - loss: 0.4698 - accuracy: 0.76 - 2s 83us/sample - loss: 0.4695 - accuracy: 0.7680\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 3s 100us/sample - loss: 0.4689 - accuracy: 0.7685\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 2s 78us/sample - loss: 0.4692 - accuracy: 0.7674\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 2s 80us/sample - loss: 0.4690 - accuracy: 0.7681\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4690 - accuracy: 0.7700\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.4686 - accuracy: 0.7683\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_9.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5000 - accuracy: 0.7538\n",
      "Loss: 0.5000275956715509, Accuracy: 0.7538192272186279\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_9.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_12\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_41 (Dense)             (None, 24)                2160      \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 12)                300       \n",
      "_________________________________________________________________\n",
      "dense_43 (Dense)             (None, 8)                 104       \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 2,573\n",
      "Trainable params: 2,573\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 24\n",
    "hidden_nodes_layer2 = 12\n",
    "hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_10 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_10.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_10.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn_10.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_10.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_10.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_10.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.5293 - accuracy: 0.7306\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 2s 71us/sample - loss: 0.4948 - accuracy: 0.7528\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 5s 177us/sample - loss: 0.4902 - accuracy: 0.7561 - loss: 0.4596 - accura - ETA: 5s - loss: 0.4588  - ETA: 7s - loss: 0.46\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 3s 103us/sample - loss: 0.4864 - accuracy: 0.7599\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4846 - accuracy: 0.7606\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 3s 104us/sample - loss: 0.4829 - accuracy: 0.7604\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 2s 88us/sample - loss: 0.4821 - accuracy: 0.7612\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 3s 115us/sample - loss: 0.4807 - accuracy: 0.7640\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 2s 66us/sample - loss: 0.4805 - accuracy: 0.7630\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 3s 104us/sample - loss: 0.4796 - accuracy: 0.7641\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4791 - accuracy: 0.7626\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 3s 128us/sample - loss: 0.4784 - accuracy: 0.7645\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 5s 187us/sample - loss: 0.4778 - accuracy: 0.7646 - loss: 0.4759 - accuracy: 0.76 - -\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 2s 66us/sample - loss: 0.4776 - accuracy: 0.7644\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 3s 108us/sample - loss: 0.4766 - accuracy: 0.7652\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4762 - accuracy: 0.7647\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 3s 100us/sample - loss: 0.4757 - accuracy: 0.7642\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 3s 103us/sample - loss: 0.4755 - accuracy: 0.7652\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.4759 - accuracy: 0.7668\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 3s 116us/sample - loss: 0.4747 - accuracy: 0.7670\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 3s 125us/sample - loss: 0.4746 - accuracy: 0.7663\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4749 - accuracy: 0.7667\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 3s 108us/sample - loss: 0.4746 - accuracy: 0.7665\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 3s 100us/sample - loss: 0.4738 - accuracy: 0.7662\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 3s 112us/sample - loss: 0.4739 - accuracy: 0.7661\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.4728 - accuracy: 0.7668\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.4731 - accuracy: 0.7670\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 3s 109us/sample - loss: 0.4727 - accuracy: 0.7663\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 3s 109us/sample - loss: 0.4726 - accuracy: 0.7673\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 3s 100us/sample - loss: 0.4719 - accuracy: 0.7675\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.4717 - accuracy: 0.7672\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 3s 129us/sample - loss: 0.4719 - accuracy: 0.7685\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 2s 76us/sample - loss: 0.4712 - accuracy: 0.7688\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.4712 - accuracy: 0.7688\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 3s 113us/sample - loss: 0.4709 - accuracy: 0.7673\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.4716 - accuracy: 0.7673\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.4709 - accuracy: 0.7675\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.4705 - accuracy: 0.7682\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 3s 130us/sample - loss: 0.4702 - accuracy: 0.7693 - loss: 0.471\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.4701 - accuracy: 0.7687\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.4701 - accuracy: 0.7687\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4700 - accuracy: 0.7683\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 3s 106us/sample - loss: 0.4696 - accuracy: 0.7690\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 3s 136us/sample - loss: 0.4693 - accuracy: 0.7684\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 2s 91us/sample - loss: 0.4691 - accuracy: 0.7685\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 4s 148us/sample - loss: 0.4685 - accuracy: 0.7685 - loss: 0.4707 - accuracy: 0.76 - ETA: 0s - loss: 0.4704 - accuracy: 0.76 - ETA: 0s - l - ETA: 0s - loss: 0.4700 \n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4688 - accuracy: 0.7701\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 2s 85us/sample - loss: 0.4690 - accuracy: 0.7693\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 4s 139us/sample - loss: 0.4684 - accuracy: 0.7710\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 3s 124us/sample - loss: 0.4684 - accuracy: 0.7696\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_10.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.4978 - accuracy: 0.7511\n",
      "Loss: 0.4977589841104457, Accuracy: 0.7511370182037354\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_10.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_13\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_45 (Dense)             (None, 24)                2160      \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 24)                600       \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 1)                 25        \n",
      "=================================================================\n",
      "Total params: 2,785\n",
      "Trainable params: 2,785\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 24\n",
    "hidden_nodes_layer2 = 24\n",
    "# hidden_nodes_layer3 = 8\n",
    "\n",
    "nn_11 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_11.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# Second hidden layer\n",
    "nn_11.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# # Third hidden layer\n",
    "# nn_11.add(tf.keras.layers.Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_11.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_11.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_11.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.5229 - accuracy: 0.7339\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 2s 65us/sample - loss: 0.4925 - accuracy: 0.7562\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 2s 63us/sample - loss: 0.4878 - accuracy: 0.7575\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 3s 106us/sample - loss: 0.4854 - accuracy: 0.7591\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.4842 - accuracy: 0.7593\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4823 - accuracy: 0.7613\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - ETA: 0s - loss: 0.4811 - accuracy: 0.7633 ETA:  - 3s 125us/sample - loss: 0.4813 - accuracy: 0.7630\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.4803 - accuracy: 0.7633\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 3s 116us/sample - loss: 0.4799 - accuracy: 0.7645\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4784 - accuracy: 0.7641\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 3s 119us/sample - loss: 0.4781 - accuracy: 0.7651\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4774 - accuracy: 0.7643\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4768 - accuracy: 0.7648\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.4755 - accuracy: 0.7645\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 3s 119us/sample - loss: 0.4757 - accuracy: 0.7655\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 80us/sample - loss: 0.4751 - accuracy: 0.7671\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.4752 - accuracy: 0.7645\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 2s 95us/sample - loss: 0.4741 - accuracy: 0.7666\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 3s 133us/sample - loss: 0.4738 - accuracy: 0.7671\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 3s 105us/sample - loss: 0.4731 - accuracy: 0.7672\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.4734 - accuracy: 0.7671\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 3s 111us/sample - loss: 0.4725 - accuracy: 0.7666\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4724 - accuracy: 0.7674\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.4721 - accuracy: 0.7680\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 3s 102us/sample - loss: 0.4717 - accuracy: 0.7675\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.4712 - accuracy: 0.7685\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 3s 118us/sample - loss: 0.4710 - accuracy: 0.7690\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4706 - accuracy: 0.7675\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 3s 114us/sample - loss: 0.4707 - accuracy: 0.7681\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.4700 - accuracy: 0.7690\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 3s 101us/sample - loss: 0.4697 - accuracy: 0.7700\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 6s 220us/sample - loss: 0.4698 - accuracy: 0.7692 - loss: 0.4699 - ac - ETA: 1s - loss: 0.4701 - ac - ETA: 1s - loss: 0.4701 - accuracy:  -\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 2s 78us/sample - loss: 0.4693 - accuracy: 0.7699\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 3s 134us/sample - loss: 0.4684 - accuracy: 0.7682\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.4691 - accuracy: 0.7698\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 2s 80us/sample - loss: 0.4682 - accuracy: 0.7710\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 3s 104us/sample - loss: 0.4684 - accuracy: 0.7700\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 3s 123us/sample - loss: 0.4681 - accuracy: 0.7701\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 4s 161us/sample - loss: 0.4673 - accuracy: 0.7714 - loss:\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 3s 122us/sample - loss: 0.4677 - accuracy: 0.7706\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 2s 89us/sample - loss: 0.4673 - accuracy: 0.7711\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.4666 - accuracy: 0.7701\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 5s 201us/sample - loss: 0.4670 - accuracy: 0.7714\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 3s 124us/sample - loss: 0.4669 - accuracy: 0.7722\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 3s 114us/sample - loss: 0.4661 - accuracy: 0.7719\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 3s 97us/sample - loss: 0.4663 - accuracy: 0.7724\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 4s 139us/sample - loss: 0.4658 - accuracy: 0.7704 - loss: 0.4658 - accuracy - ETA: 1s - loss: 0.4664 - accu\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4659 - accuracy: 0.7719\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.4654 - accuracy: 0.7724\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 3s 130us/sample - loss: 0.4657 - accuracy: 0.7722\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_11.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.5032 - accuracy: 0.7514\n",
      "Loss: 0.5032284814692795, Accuracy: 0.7513702511787415\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_11.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-12\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_15\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_50 (Dense)             (None, 36)                3240      \n",
      "_________________________________________________________________\n",
      "dense_51 (Dense)             (None, 1)                 37        \n",
      "=================================================================\n",
      "Total params: 3,277\n",
      "Trainable params: 3,277\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 36\n",
    "# hidden_nodes_layer2 = 12\n",
    "\n",
    "nn_12 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_12.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# # Second hidden layer\n",
    "# nn_12.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_12.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_12.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_12.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 76us/sample - loss: 0.5202 - accuracy: 0.7340\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 3s 131us/sample - loss: 0.4942 - accuracy: 0.7548\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4904 - accuracy: 0.7573\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 2s 79us/sample - loss: 0.4887 - accuracy: 0.7581\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 3s 105us/sample - loss: 0.4862 - accuracy: 0.7605\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4863 - accuracy: 0.7598\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 5s 179us/sample - loss: 0.4845 - accuracy: 0.7602\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 2s 88us/sample - loss: 0.4843 - accuracy: 0.7597\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.4833 - accuracy: 0.7621\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4829 - accuracy: 0.7607\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4822 - accuracy: 0.7603\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.4821 - accuracy: 0.7617\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 3s 130us/sample - loss: 0.4811 - accuracy: 0.7610\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.4816 - accuracy: 0.7621\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.4811 - accuracy: 0.7621\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 2s 68us/sample - loss: 0.4801 - accuracy: 0.7635\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.4796 - accuracy: 0.7635\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.4798 - accuracy: 0.7644\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.4799 - accuracy: 0.7632\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 2s 80us/sample - loss: 0.4798 - accuracy: 0.7631\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 3s 117us/sample - loss: 0.4788 - accuracy: 0.7624\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 2s 90us/sample - loss: 0.4790 - accuracy: 0.7629\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4784 - accuracy: 0.7638\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.4787 - accuracy: 0.7637\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4776 - accuracy: 0.7642\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 3s 119us/sample - loss: 0.4783 - accuracy: 0.7638\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4779 - accuracy: 0.7640\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 3s 126us/sample - loss: 0.4779 - accuracy: 0.7652 - loss: 0.4762 -  - ETA\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 3s 132us/sample - loss: 0.4777 - accuracy: 0.7629 - loss: 0.4806 - accu - ETA: 0s - l\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 3s 123us/sample - loss: 0.4777 - accuracy: 0.7649 - loss: 0.4761 - accuracy:  - ETA: 1s - loss: - ETA: 1s - los\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 3s 109us/sample - loss: 0.4774 - accuracy: 0.7629\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4770 - accuracy: 0.7637\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 2s 70us/sample - loss: 0.4772 - accuracy: 0.7641\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 3s 100us/sample - loss: 0.4767 - accuracy: 0.7628\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 3s 122us/sample - loss: 0.4772 - accuracy: 0.7640\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 3s 109us/sample - loss: 0.4769 - accuracy: 0.7644\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 3s 113us/sample - loss: 0.4763 - accuracy: 0.7650\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.4767 - accuracy: 0.7642\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 2s 88us/sample - loss: 0.4759 - accuracy: 0.7645\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 3s 115us/sample - loss: 0.4762 - accuracy: 0.7642\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 3s 131us/sample - loss: 0.4755 - accuracy: 0.7655\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 4s 145us/sample - loss: 0.4757 - accuracy: 0.7651\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 3s 120us/sample - loss: 0.4758 - accuracy: 0.7647\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 2s 77us/sample - loss: 0.4754 - accuracy: 0.7647\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 4s 142us/sample - loss: 0.4754 - accuracy: 0.7651\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4753 - accuracy: 0.7664\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4751 - accuracy: 0.7655\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 3s 103us/sample - loss: 0.4752 - accuracy: 0.7645\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 3s 112us/sample - loss: 0.4745 - accuracy: 0.7668\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 2s 78us/sample - loss: 0.4747 - accuracy: 0.7650\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_12.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.4962 - accuracy: 0.7520\n",
      "Loss: 0.4962383259871958, Accuracy: 0.7519533634185791\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_12.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model-13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_52 (Dense)             (None, 24)                2160      \n",
      "_________________________________________________________________\n",
      "dense_53 (Dense)             (None, 1)                 25        \n",
      "=================================================================\n",
      "Total params: 2,185\n",
      "Trainable params: 2,185\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 = 24\n",
    "# hidden_nodes_layer2 = 12\n",
    "\n",
    "nn_13 = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn_13.add(tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\"))\n",
    "\n",
    "# # Second hidden layer\n",
    "# nn_12.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Output layer\n",
    "nn_13.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn_13.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn_13.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25724 samples\n",
      "Epoch 1/50\n",
      "25724/25724 [==============================] - 2s 73us/sample - loss: 0.5302 - accuracy: 0.7265\n",
      "Epoch 2/50\n",
      "25724/25724 [==============================] - 2s 60us/sample - loss: 0.4965 - accuracy: 0.7496\n",
      "Epoch 3/50\n",
      "25724/25724 [==============================] - 2s 63us/sample - loss: 0.4915 - accuracy: 0.7537\n",
      "Epoch 4/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4894 - accuracy: 0.7550\n",
      "Epoch 5/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.4871 - accuracy: 0.7567\n",
      "Epoch 6/50\n",
      "25724/25724 [==============================] - 2s 79us/sample - loss: 0.4864 - accuracy: 0.7577\n",
      "Epoch 7/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4853 - accuracy: 0.7595\n",
      "Epoch 8/50\n",
      "25724/25724 [==============================] - 5s 177us/sample - loss: 0.4844 - accuracy: 0.7598 - - - ETA: 1s - loss: 0.4857 - accuracy:  - ETA: 1s - l\n",
      "Epoch 9/50\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.4840 - accuracy: 0.7598\n",
      "Epoch 10/50\n",
      "25724/25724 [==============================] - 3s 104us/sample - loss: 0.4834 - accuracy: 0.7594\n",
      "Epoch 11/50\n",
      "25724/25724 [==============================] - 3s 125us/sample - loss: 0.4829 - accuracy: 0.7614 - loss: 0.4827 - accuracy - ETA: 1s - loss: 0.4824 - accuracy:  - ETA: 1s - loss:\n",
      "Epoch 12/50\n",
      "25724/25724 [==============================] - 3s 118us/sample - loss: 0.4825 - accuracy: 0.7600\n",
      "Epoch 13/50\n",
      "25724/25724 [==============================] - 3s 129us/sample - loss: 0.4821 - accuracy: 0.7614\n",
      "Epoch 14/50\n",
      "25724/25724 [==============================] - 3s 100us/sample - loss: 0.4810 - accuracy: 0.7621\n",
      "Epoch 15/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4811 - accuracy: 0.7612\n",
      "Epoch 16/50\n",
      "25724/25724 [==============================] - 3s 110us/sample - loss: 0.4807 - accuracy: 0.7628\n",
      "Epoch 17/50\n",
      "25724/25724 [==============================] - 2s 74us/sample - loss: 0.4801 - accuracy: 0.7618\n",
      "Epoch 18/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4799 - accuracy: 0.7601\n",
      "Epoch 19/50\n",
      "25724/25724 [==============================] - 3s 128us/sample - loss: 0.4797 - accuracy: 0.7630\n",
      "Epoch 20/50\n",
      "25724/25724 [==============================] - 3s 113us/sample - loss: 0.4795 - accuracy: 0.7614\n",
      "Epoch 21/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4789 - accuracy: 0.7630\n",
      "Epoch 22/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4789 - accuracy: 0.7636\n",
      "Epoch 23/50\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.4787 - accuracy: 0.7629\n",
      "Epoch 24/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.4785 - accuracy: 0.7631\n",
      "Epoch 25/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.4779 - accuracy: 0.7633\n",
      "Epoch 26/50\n",
      "25724/25724 [==============================] - 3s 117us/sample - loss: 0.4781 - accuracy: 0.7624\n",
      "Epoch 27/50\n",
      "25724/25724 [==============================] - 2s 72us/sample - loss: 0.4776 - accuracy: 0.7629\n",
      "Epoch 28/50\n",
      "25724/25724 [==============================] - 3s 120us/sample - loss: 0.4774 - accuracy: 0.7631 - los\n",
      "Epoch 29/50\n",
      "25724/25724 [==============================] - 2s 86us/sample - loss: 0.4772 - accuracy: 0.7637\n",
      "Epoch 30/50\n",
      "25724/25724 [==============================] - 3s 99us/sample - loss: 0.4775 - accuracy: 0.7643\n",
      "Epoch 31/50\n",
      "25724/25724 [==============================] - 2s 82us/sample - loss: 0.4773 - accuracy: 0.7644\n",
      "Epoch 32/50\n",
      "25724/25724 [==============================] - 3s 98us/sample - loss: 0.4771 - accuracy: 0.7635\n",
      "Epoch 33/50\n",
      "25724/25724 [==============================] - 3s 115us/sample - loss: 0.4767 - accuracy: 0.7645\n",
      "Epoch 34/50\n",
      "25724/25724 [==============================] - 2s 83us/sample - loss: 0.4767 - accuracy: 0.7642\n",
      "Epoch 35/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4765 - accuracy: 0.7636\n",
      "Epoch 36/50\n",
      "25724/25724 [==============================] - 3s 108us/sample - loss: 0.4763 - accuracy: 0.7639\n",
      "Epoch 37/50\n",
      "25724/25724 [==============================] - 2s 92us/sample - loss: 0.4762 - accuracy: 0.7650\n",
      "Epoch 38/50\n",
      "25724/25724 [==============================] - 2s 79us/sample - loss: 0.4761 - accuracy: 0.7645\n",
      "Epoch 39/50\n",
      "25724/25724 [==============================] - 3s 123us/sample - loss: 0.4761 - accuracy: 0.7641\n",
      "Epoch 40/50\n",
      "25724/25724 [==============================] - 2s 67us/sample - loss: 0.4756 - accuracy: 0.7635\n",
      "Epoch 41/50\n",
      "25724/25724 [==============================] - 3s 103us/sample - loss: 0.4754 - accuracy: 0.7639\n",
      "Epoch 42/50\n",
      "25724/25724 [==============================] - 2s 93us/sample - loss: 0.4759 - accuracy: 0.7644\n",
      "Epoch 43/50\n",
      "25724/25724 [==============================] - 3s 107us/sample - loss: 0.4752 - accuracy: 0.7653\n",
      "Epoch 44/50\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.4755 - accuracy: 0.7647\n",
      "Epoch 45/50\n",
      "25724/25724 [==============================] - 2s 75us/sample - loss: 0.4756 - accuracy: 0.7652\n",
      "Epoch 46/50\n",
      "25724/25724 [==============================] - 3s 120us/sample - loss: 0.4752 - accuracy: 0.7643\n",
      "Epoch 47/50\n",
      "25724/25724 [==============================] - 2s 94us/sample - loss: 0.4749 - accuracy: 0.7655\n",
      "Epoch 48/50\n",
      "25724/25724 [==============================] - 3s 103us/sample - loss: 0.4748 - accuracy: 0.7638\n",
      "Epoch 49/50\n",
      "25724/25724 [==============================] - 2s 96us/sample - loss: 0.4750 - accuracy: 0.7641\n",
      "Epoch 50/50\n",
      "25724/25724 [==============================] - 2s 97us/sample - loss: 0.4746 - accuracy: 0.7648\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn_13.fit(X_train_scaled,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8575/8575 - 0s - loss: 0.4971 - accuracy: 0.7482\n",
      "Loss: 0.497126927358416, Accuracy: 0.7482215762138367\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn_13.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": []
  }
 ],
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