exoplanets_deeplearning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sklearn\n",
      "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn in c:\\users\\pengu\\anaconda3\\lib\\site-packages (from sklearn) (0.22.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.11.0 in c:\\users\\pengu\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.18.1)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\pengu\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.14.1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.17.0 in c:\\users\\pengu\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.4.1)\n",
      "Building wheels for collected packages: sklearn\n",
      "  Building wheel for sklearn (setup.py): started\n",
      "  Building wheel for sklearn (setup.py): finished with status 'done'\n",
      "  Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1320 sha256=05df7932788cc241896257d365ed4d8646f91e6879419068b07fa89fd50e6e3e\n",
      "  Stored in directory: c:\\users\\pengu\\appdata\\local\\pip\\cache\\wheels\\46\\ef\\c3\\157e41f5ee1372d1be90b09f74f82b10e391eaacca8f22d33e\n",
      "Successfully built sklearn\n",
      "Installing collected packages: sklearn\n",
      "Successfully installed sklearn-0.0\n"
     ]
    }
   ],
   "source": [
    "# Update sklearn to prevent version mismatches\n",
    "!pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: joblib in c:\\users\\pengu\\anaconda3\\lib\\site-packages (0.14.1)\n"
     ]
    }
   ],
   "source": [
    "# install joblib. This will be used to save your model. \n",
    "# Restart your kernel after installing \n",
    "!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>koi_disposition</th>\n",
       "      <th>koi_fpflag_nt</th>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
       "      <th>koi_period</th>\n",
       "      <th>koi_period_err1</th>\n",
       "      <th>koi_period_err2</th>\n",
       "      <th>koi_time0bk</th>\n",
       "      <th>koi_time0bk_err1</th>\n",
       "      <th>...</th>\n",
       "      <th>koi_steff_err2</th>\n",
       "      <th>koi_slogg</th>\n",
       "      <th>koi_slogg_err1</th>\n",
       "      <th>koi_slogg_err2</th>\n",
       "      <th>koi_srad</th>\n",
       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>koi_kepmag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.418383</td>\n",
       "      <td>2.479000e-04</td>\n",
       "      <td>-2.479000e-04</td>\n",
       "      <td>162.513840</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>...</td>\n",
       "      <td>-81</td>\n",
       "      <td>4.467</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.105</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>291.93423</td>\n",
       "      <td>48.141651</td>\n",
       "      <td>15.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.899140</td>\n",
       "      <td>1.490000e-05</td>\n",
       "      <td>-1.490000e-05</td>\n",
       "      <td>175.850252</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>...</td>\n",
       "      <td>-176</td>\n",
       "      <td>4.544</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.233</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>297.00482</td>\n",
       "      <td>48.134129</td>\n",
       "      <td>15.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.736952</td>\n",
       "      <td>2.630000e-07</td>\n",
       "      <td>-2.630000e-07</td>\n",
       "      <td>170.307565</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>...</td>\n",
       "      <td>-174</td>\n",
       "      <td>4.564</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.168</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>285.53461</td>\n",
       "      <td>48.285210</td>\n",
       "      <td>15.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.525592</td>\n",
       "      <td>3.760000e-06</td>\n",
       "      <td>-3.760000e-06</td>\n",
       "      <td>171.595550</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>...</td>\n",
       "      <td>-211</td>\n",
       "      <td>4.438</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>1.046</td>\n",
       "      <td>0.334</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>288.75488</td>\n",
       "      <td>48.226200</td>\n",
       "      <td>15.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.134435</td>\n",
       "      <td>1.050000e-05</td>\n",
       "      <td>-1.050000e-05</td>\n",
       "      <td>172.979370</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>...</td>\n",
       "      <td>-232</td>\n",
       "      <td>4.486</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.315</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>296.28613</td>\n",
       "      <td>48.224670</td>\n",
       "      <td>15.714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  koi_disposition  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  \\\n",
       "0       CONFIRMED              0              0              0              0   \n",
       "1  FALSE POSITIVE              0              1              0              0   \n",
       "2  FALSE POSITIVE              0              1              0              0   \n",
       "3       CONFIRMED              0              0              0              0   \n",
       "4       CONFIRMED              0              0              0              0   \n",
       "\n",
       "   koi_period  koi_period_err1  koi_period_err2  koi_time0bk  \\\n",
       "0   54.418383     2.479000e-04    -2.479000e-04   162.513840   \n",
       "1   19.899140     1.490000e-05    -1.490000e-05   175.850252   \n",
       "2    1.736952     2.630000e-07    -2.630000e-07   170.307565   \n",
       "3    2.525592     3.760000e-06    -3.760000e-06   171.595550   \n",
       "4    4.134435     1.050000e-05    -1.050000e-05   172.979370   \n",
       "\n",
       "   koi_time0bk_err1  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "0          0.003520  ...             -81      4.467           0.064   \n",
       "1          0.000581  ...            -176      4.544           0.044   \n",
       "2          0.000115  ...            -174      4.564           0.053   \n",
       "3          0.001130  ...            -211      4.438           0.070   \n",
       "4          0.001900  ...            -232      4.486           0.054   \n",
       "\n",
       "   koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "0          -0.096     0.927          0.105         -0.061  291.93423   \n",
       "1          -0.176     0.868          0.233         -0.078  297.00482   \n",
       "2          -0.168     0.791          0.201         -0.067  285.53461   \n",
       "3          -0.210     1.046          0.334         -0.133  288.75488   \n",
       "4          -0.229     0.972          0.315         -0.105  296.28613   \n",
       "\n",
       "         dec  koi_kepmag  \n",
       "0  48.141651      15.347  \n",
       "1  48.134129      15.436  \n",
       "2  48.285210      15.597  \n",
       "3  48.226200      15.509  \n",
       "4  48.224670      15.714  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/exoplanet_data.csv\")\n",
    "# Drop the null columns where all values are null\n",
    "df = df.dropna(axis='columns', how='all')\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set features. This will also be used as your x values.\n",
    "# selected_features = df[['names', 'of', 'selected', 'features', 'here']]\n",
    "selected_features = df.drop(\"koi_disposition\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `koi_disposition` for the y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6991, 40) (6991,)\n"
     ]
    }
   ],
   "source": [
    "y = df[\"koi_disposition\"]\n",
    "print(selected_features.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>koi_fpflag_nt</th>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
       "      <th>koi_period</th>\n",
       "      <th>koi_period_err1</th>\n",
       "      <th>koi_period_err2</th>\n",
       "      <th>koi_time0bk</th>\n",
       "      <th>koi_time0bk_err1</th>\n",
       "      <th>koi_time0bk_err2</th>\n",
       "      <th>...</th>\n",
       "      <th>koi_steff_err2</th>\n",
       "      <th>koi_slogg</th>\n",
       "      <th>koi_slogg_err1</th>\n",
       "      <th>koi_slogg_err2</th>\n",
       "      <th>koi_srad</th>\n",
       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>koi_kepmag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.548413</td>\n",
       "      <td>5.470000e-05</td>\n",
       "      <td>-5.470000e-05</td>\n",
       "      <td>139.064020</td>\n",
       "      <td>0.004110</td>\n",
       "      <td>-0.004110</td>\n",
       "      <td>...</td>\n",
       "      <td>-133</td>\n",
       "      <td>4.387</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>1.092</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>298.09543</td>\n",
       "      <td>44.737061</td>\n",
       "      <td>13.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4099</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.754385</td>\n",
       "      <td>1.365000e-04</td>\n",
       "      <td>-1.365000e-04</td>\n",
       "      <td>140.207320</td>\n",
       "      <td>0.004460</td>\n",
       "      <td>-0.004460</td>\n",
       "      <td>...</td>\n",
       "      <td>-144</td>\n",
       "      <td>4.519</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>295.73535</td>\n",
       "      <td>42.576248</td>\n",
       "      <td>15.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5460</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.057336</td>\n",
       "      <td>1.230000e-07</td>\n",
       "      <td>-1.230000e-07</td>\n",
       "      <td>131.792007</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>...</td>\n",
       "      <td>-140</td>\n",
       "      <td>4.594</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>292.18417</td>\n",
       "      <td>49.310040</td>\n",
       "      <td>15.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201.118319</td>\n",
       "      <td>1.461000e-03</td>\n",
       "      <td>-1.461000e-03</td>\n",
       "      <td>187.569860</td>\n",
       "      <td>0.005290</td>\n",
       "      <td>-0.005290</td>\n",
       "      <td>...</td>\n",
       "      <td>-112</td>\n",
       "      <td>4.447</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.135</td>\n",
       "      <td>-0.083</td>\n",
       "      <td>283.11377</td>\n",
       "      <td>48.131390</td>\n",
       "      <td>13.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.649983</td>\n",
       "      <td>3.181000e-03</td>\n",
       "      <td>-3.181000e-03</td>\n",
       "      <td>175.715600</td>\n",
       "      <td>0.028600</td>\n",
       "      <td>-0.028600</td>\n",
       "      <td>...</td>\n",
       "      <td>-233</td>\n",
       "      <td>4.145</td>\n",
       "      <td>0.164</td>\n",
       "      <td>-0.164</td>\n",
       "      <td>1.608</td>\n",
       "      <td>0.905</td>\n",
       "      <td>-0.383</td>\n",
       "      <td>294.93198</td>\n",
       "      <td>39.812420</td>\n",
       "      <td>12.964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  koi_period  \\\n",
       "3563              0              0              0              0   10.548413   \n",
       "4099              0              0              0              0   24.754385   \n",
       "5460              0              0              0              0    1.057336   \n",
       "1091              0              0              0              0  201.118319   \n",
       "5999              0              0              0              0   91.649983   \n",
       "\n",
       "      koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "3563     5.470000e-05    -5.470000e-05   139.064020          0.004110   \n",
       "4099     1.365000e-04    -1.365000e-04   140.207320          0.004460   \n",
       "5460     1.230000e-07    -1.230000e-07   131.792007          0.000096   \n",
       "1091     1.461000e-03    -1.461000e-03   187.569860          0.005290   \n",
       "5999     3.181000e-03    -3.181000e-03   175.715600          0.028600   \n",
       "\n",
       "      koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "3563         -0.004110  ...            -133      4.387           0.066   \n",
       "4099         -0.004460  ...            -144      4.519           0.078   \n",
       "5460         -0.000096  ...            -140      4.594           0.054   \n",
       "1091         -0.005290  ...            -112      4.447           0.072   \n",
       "5999         -0.028600  ...            -233      4.145           0.164   \n",
       "\n",
       "      koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "3563          -0.123     1.092          0.181         -0.097  298.09543   \n",
       "4099          -0.052     0.804          0.056         -0.076  295.73535   \n",
       "5460          -0.027     0.683          0.054         -0.060  292.18417   \n",
       "1091          -0.108     0.954          0.135         -0.083  283.11377   \n",
       "5999          -0.164     1.608          0.905         -0.383  294.93198   \n",
       "\n",
       "            dec  koi_kepmag  \n",
       "3563  44.737061      13.204  \n",
       "4099  42.576248      15.514  \n",
       "5460  49.310040      15.414  \n",
       "1091  48.131390      13.328  \n",
       "5999  39.812420      12.964  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "X_train, X_test, y_train, y_test = train_test_split(selected_features, y, random_state=1)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "\n",
    "Scale the data using the MinMaxScaler and perform some feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale your data\n",
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "\n",
    "# Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)\n",
    "\n",
    "# Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "164/164 - 0s - loss: 0.7251 - accuracy: 0.6409\n",
      "Epoch 2/100\n",
      "164/164 - 0s - loss: 0.3918 - accuracy: 0.8013\n",
      "Epoch 3/100\n",
      "164/164 - 0s - loss: 0.3702 - accuracy: 0.8089\n",
      "Epoch 4/100\n",
      "164/164 - 0s - loss: 0.3601 - accuracy: 0.8112\n",
      "Epoch 5/100\n",
      "164/164 - 0s - loss: 0.3554 - accuracy: 0.8203\n",
      "Epoch 6/100\n",
      "164/164 - 0s - loss: 0.3461 - accuracy: 0.8293\n",
      "Epoch 7/100\n",
      "164/164 - 0s - loss: 0.3419 - accuracy: 0.8316\n",
      "Epoch 8/100\n",
      "164/164 - 0s - loss: 0.3361 - accuracy: 0.8449\n",
      "Epoch 9/100\n",
      "164/164 - 0s - loss: 0.3329 - accuracy: 0.8386\n",
      "Epoch 10/100\n",
      "164/164 - 0s - loss: 0.3298 - accuracy: 0.8438\n",
      "Epoch 11/100\n",
      "164/164 - 0s - loss: 0.3277 - accuracy: 0.8430\n",
      "Epoch 12/100\n",
      "164/164 - 0s - loss: 0.3249 - accuracy: 0.8463\n",
      "Epoch 13/100\n",
      "164/164 - 0s - loss: 0.3171 - accuracy: 0.8558\n",
      "Epoch 14/100\n",
      "164/164 - 0s - loss: 0.3167 - accuracy: 0.8541\n",
      "Epoch 15/100\n",
      "164/164 - 0s - loss: 0.3114 - accuracy: 0.8554\n",
      "Epoch 16/100\n",
      "164/164 - 0s - loss: 0.3099 - accuracy: 0.8545\n",
      "Epoch 17/100\n",
      "164/164 - 0s - loss: 0.3079 - accuracy: 0.8606\n",
      "Epoch 18/100\n",
      "164/164 - 0s - loss: 0.3072 - accuracy: 0.8617\n",
      "Epoch 19/100\n",
      "164/164 - 0s - loss: 0.3012 - accuracy: 0.8632\n",
      "Epoch 20/100\n",
      "164/164 - 0s - loss: 0.2994 - accuracy: 0.8699\n",
      "Epoch 21/100\n",
      "164/164 - 0s - loss: 0.2964 - accuracy: 0.8669\n",
      "Epoch 22/100\n",
      "164/164 - 0s - loss: 0.2958 - accuracy: 0.8693\n",
      "Epoch 23/100\n",
      "164/164 - 0s - loss: 0.2980 - accuracy: 0.8648\n",
      "Epoch 24/100\n",
      "164/164 - 0s - loss: 0.2935 - accuracy: 0.8709\n",
      "Epoch 25/100\n",
      "164/164 - 0s - loss: 0.2890 - accuracy: 0.8753\n",
      "Epoch 26/100\n",
      "164/164 - 0s - loss: 0.2907 - accuracy: 0.8718\n",
      "Epoch 27/100\n",
      "164/164 - 0s - loss: 0.2914 - accuracy: 0.8709\n",
      "Epoch 28/100\n",
      "164/164 - 0s - loss: 0.2893 - accuracy: 0.8722\n",
      "Epoch 29/100\n",
      "164/164 - 0s - loss: 0.2853 - accuracy: 0.8758\n",
      "Epoch 30/100\n",
      "164/164 - 0s - loss: 0.2871 - accuracy: 0.8739\n",
      "Epoch 31/100\n",
      "164/164 - 0s - loss: 0.2812 - accuracy: 0.8781\n",
      "Epoch 32/100\n",
      "164/164 - 0s - loss: 0.2818 - accuracy: 0.8732\n",
      "Epoch 33/100\n",
      "164/164 - 0s - loss: 0.2866 - accuracy: 0.8737\n",
      "Epoch 34/100\n",
      "164/164 - 0s - loss: 0.2794 - accuracy: 0.8812\n",
      "Epoch 35/100\n",
      "164/164 - 0s - loss: 0.2757 - accuracy: 0.8827\n",
      "Epoch 36/100\n",
      "164/164 - 0s - loss: 0.2813 - accuracy: 0.8774\n",
      "Epoch 37/100\n",
      "164/164 - 0s - loss: 0.2759 - accuracy: 0.8816\n",
      "Epoch 38/100\n",
      "164/164 - 0s - loss: 0.2826 - accuracy: 0.8745\n",
      "Epoch 39/100\n",
      "164/164 - 0s - loss: 0.2742 - accuracy: 0.8812\n",
      "Epoch 40/100\n",
      "164/164 - 0s - loss: 0.2726 - accuracy: 0.8787\n",
      "Epoch 41/100\n",
      "164/164 - 0s - loss: 0.2728 - accuracy: 0.8837\n",
      "Epoch 42/100\n",
      "164/164 - 0s - loss: 0.2756 - accuracy: 0.8808\n",
      "Epoch 43/100\n",
      "164/164 - 0s - loss: 0.2675 - accuracy: 0.8865\n",
      "Epoch 44/100\n",
      "164/164 - 0s - loss: 0.2718 - accuracy: 0.8819\n",
      "Epoch 45/100\n",
      "164/164 - 0s - loss: 0.2678 - accuracy: 0.8892\n",
      "Epoch 46/100\n",
      "164/164 - 0s - loss: 0.2696 - accuracy: 0.8838\n",
      "Epoch 47/100\n",
      "164/164 - 0s - loss: 0.2680 - accuracy: 0.8856\n",
      "Epoch 48/100\n",
      "164/164 - 0s - loss: 0.2689 - accuracy: 0.8861\n",
      "Epoch 49/100\n",
      "164/164 - 0s - loss: 0.2675 - accuracy: 0.8835\n",
      "Epoch 50/100\n",
      "164/164 - 0s - loss: 0.2682 - accuracy: 0.8859\n",
      "Epoch 51/100\n",
      "164/164 - 0s - loss: 0.2659 - accuracy: 0.8854\n",
      "Epoch 52/100\n",
      "164/164 - 0s - loss: 0.2670 - accuracy: 0.8838\n",
      "Epoch 53/100\n",
      "164/164 - 0s - loss: 0.2685 - accuracy: 0.8825\n",
      "Epoch 54/100\n",
      "164/164 - 0s - loss: 0.2591 - accuracy: 0.8898\n",
      "Epoch 55/100\n",
      "164/164 - 0s - loss: 0.2636 - accuracy: 0.8871\n",
      "Epoch 56/100\n",
      "164/164 - 0s - loss: 0.2608 - accuracy: 0.8880\n",
      "Epoch 57/100\n",
      "164/164 - 0s - loss: 0.2569 - accuracy: 0.8922\n",
      "Epoch 58/100\n",
      "164/164 - 0s - loss: 0.2696 - accuracy: 0.8816\n",
      "Epoch 59/100\n",
      "164/164 - 0s - loss: 0.2577 - accuracy: 0.8896\n",
      "Epoch 60/100\n",
      "164/164 - 0s - loss: 0.2555 - accuracy: 0.8896\n",
      "Epoch 61/100\n",
      "164/164 - 0s - loss: 0.2605 - accuracy: 0.8858\n",
      "Epoch 62/100\n",
      "164/164 - 0s - loss: 0.2585 - accuracy: 0.8884\n",
      "Epoch 63/100\n",
      "164/164 - 0s - loss: 0.2554 - accuracy: 0.8898\n",
      "Epoch 64/100\n",
      "164/164 - 0s - loss: 0.2532 - accuracy: 0.8919\n",
      "Epoch 65/100\n",
      "164/164 - 0s - loss: 0.2611 - accuracy: 0.8869\n",
      "Epoch 66/100\n",
      "164/164 - 0s - loss: 0.2538 - accuracy: 0.8871\n",
      "Epoch 67/100\n",
      "164/164 - 0s - loss: 0.2517 - accuracy: 0.8938\n",
      "Epoch 68/100\n",
      "164/164 - 0s - loss: 0.2538 - accuracy: 0.8898\n",
      "Epoch 69/100\n",
      "164/164 - 0s - loss: 0.2524 - accuracy: 0.8899\n",
      "Epoch 70/100\n",
      "164/164 - 0s - loss: 0.2522 - accuracy: 0.8932\n",
      "Epoch 71/100\n",
      "164/164 - 0s - loss: 0.2479 - accuracy: 0.8947\n",
      "Epoch 72/100\n",
      "164/164 - 0s - loss: 0.2531 - accuracy: 0.8899\n",
      "Epoch 73/100\n",
      "164/164 - 0s - loss: 0.2515 - accuracy: 0.8856\n",
      "Epoch 74/100\n",
      "164/164 - 0s - loss: 0.2499 - accuracy: 0.8901\n",
      "Epoch 75/100\n",
      "164/164 - 0s - loss: 0.2568 - accuracy: 0.8886\n",
      "Epoch 76/100\n",
      "164/164 - 0s - loss: 0.2486 - accuracy: 0.8932\n",
      "Epoch 77/100\n",
      "164/164 - 0s - loss: 0.2475 - accuracy: 0.8920\n",
      "Epoch 78/100\n",
      "164/164 - 0s - loss: 0.2460 - accuracy: 0.8911\n",
      "Epoch 79/100\n",
      "164/164 - 0s - loss: 0.2455 - accuracy: 0.8961\n",
      "Epoch 80/100\n",
      "164/164 - 0s - loss: 0.2452 - accuracy: 0.8917\n",
      "Epoch 81/100\n",
      "164/164 - 0s - loss: 0.2470 - accuracy: 0.8917\n",
      "Epoch 82/100\n",
      "164/164 - 0s - loss: 0.2437 - accuracy: 0.8936\n",
      "Epoch 83/100\n",
      "164/164 - 0s - loss: 0.2466 - accuracy: 0.8928\n",
      "Epoch 84/100\n",
      "164/164 - 0s - loss: 0.2421 - accuracy: 0.8934\n",
      "Epoch 85/100\n",
      "164/164 - 0s - loss: 0.2471 - accuracy: 0.8928\n",
      "Epoch 86/100\n",
      "164/164 - 0s - loss: 0.2427 - accuracy: 0.8917\n",
      "Epoch 87/100\n",
      "164/164 - 0s - loss: 0.2411 - accuracy: 0.8970\n",
      "Epoch 88/100\n",
      "164/164 - 0s - loss: 0.2412 - accuracy: 0.8964\n",
      "Epoch 89/100\n",
      "164/164 - 0s - loss: 0.2392 - accuracy: 0.8959\n",
      "Epoch 90/100\n",
      "164/164 - 0s - loss: 0.2442 - accuracy: 0.8922\n",
      "Epoch 91/100\n",
      "164/164 - 0s - loss: 0.2391 - accuracy: 0.8930\n",
      "Epoch 92/100\n",
      "164/164 - 0s - loss: 0.2361 - accuracy: 0.8972\n",
      "Epoch 93/100\n",
      "164/164 - 0s - loss: 0.2376 - accuracy: 0.8983\n",
      "Epoch 94/100\n",
      "164/164 - 0s - loss: 0.2378 - accuracy: 0.8959\n",
      "Epoch 95/100\n",
      "164/164 - 0s - loss: 0.2372 - accuracy: 0.8978\n",
      "Epoch 96/100\n",
      "164/164 - 0s - loss: 0.2399 - accuracy: 0.8938\n",
      "Epoch 97/100\n",
      "164/164 - 0s - loss: 0.2419 - accuracy: 0.8943\n",
      "Epoch 98/100\n",
      "164/164 - 0s - loss: 0.2409 - accuracy: 0.8938\n",
      "Epoch 99/100\n",
      "164/164 - 0s - loss: 0.2407 - accuracy: 0.8924\n",
      "Epoch 100/100\n",
      "164/164 - 0s - loss: 0.2400 - accuracy: 0.8968\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2d7c1111cc8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=50, activation='relu', input_dim=40))\n",
    "model.add(Dense(units=50, activation='relu'))\n",
    "model.add(Dense(units=3, activation='softmax'))\n",
    "\n",
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# model.summary()\n",
    "\n",
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=100,\n",
    "    shuffle=True,\n",
    "    verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantify the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 - 0s - loss: 0.2355 - accuracy: 0.9050\n",
      "Normal Neural Network - Loss: 0.2355320304632187, Accuracy: 0.9050343036651611\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-16-2a9c244b30ec>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "Predicted classes: ['CONFIRMED' 'FALSE POSITIVE' 'FALSE POSITIVE' 'CONFIRMED'\n",
      " 'FALSE POSITIVE']\n",
      "Actual Labels: ['CONFIRMED', 'FALSE POSITIVE', 'FALSE POSITIVE', 'CONFIRMED', 'FALSE POSITIVE']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "encoded_predictions = model.predict_classes(X_test_scaled[:5])\n",
    "prediction_labels = label_encoder.inverse_transform(encoded_predictions)\n",
    "print(f\"Predicted classes: {prediction_labels}\")\n",
    "print(f\"Actual Labels: {list(y_test[:5])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"Deeplearning_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "dev"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "nteract": {
   "version": "0.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}