EPRandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update sklearn to prevent version mismatches\n",
    "#!pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Dependencies\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>6986</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.589871</td>\n",
       "      <td>1.846000e-04</td>\n",
       "      <td>-1.846000e-04</td>\n",
       "      <td>132.016100</td>\n",
       "      <td>0.015700</td>\n",
       "      <td>...</td>\n",
       "      <td>-152</td>\n",
       "      <td>4.296</td>\n",
       "      <td>0.231</td>\n",
       "      <td>-0.189</td>\n",
       "      <td>1.088</td>\n",
       "      <td>0.313</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>298.74921</td>\n",
       "      <td>46.973351</td>\n",
       "      <td>14.478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6987</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.527699</td>\n",
       "      <td>1.160000e-07</td>\n",
       "      <td>-1.160000e-07</td>\n",
       "      <td>131.705093</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>...</td>\n",
       "      <td>-166</td>\n",
       "      <td>4.529</td>\n",
       "      <td>0.035</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.237</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>297.18875</td>\n",
       "      <td>47.093819</td>\n",
       "      <td>14.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6988</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1.739849</td>\n",
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       "      <td>-1.780000e-05</td>\n",
       "      <td>133.001270</td>\n",
       "      <td>0.007690</td>\n",
       "      <td>...</td>\n",
       "      <td>-220</td>\n",
       "      <td>4.444</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>1.031</td>\n",
       "      <td>0.341</td>\n",
       "      <td>-0.114</td>\n",
       "      <td>286.50937</td>\n",
       "      <td>47.163219</td>\n",
       "      <td>14.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.681402</td>\n",
       "      <td>2.430000e-06</td>\n",
       "      <td>-2.430000e-06</td>\n",
       "      <td>132.181750</td>\n",
       "      <td>0.002850</td>\n",
       "      <td>...</td>\n",
       "      <td>-236</td>\n",
       "      <td>4.447</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.224</td>\n",
       "      <td>1.041</td>\n",
       "      <td>0.341</td>\n",
       "      <td>-0.114</td>\n",
       "      <td>294.16489</td>\n",
       "      <td>47.176281</td>\n",
       "      <td>15.385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6990</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.856035</td>\n",
       "      <td>6.360000e-05</td>\n",
       "      <td>-6.360000e-05</td>\n",
       "      <td>135.993300</td>\n",
       "      <td>0.010800</td>\n",
       "      <td>...</td>\n",
       "      <td>-225</td>\n",
       "      <td>4.385</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>1.193</td>\n",
       "      <td>0.410</td>\n",
       "      <td>-0.137</td>\n",
       "      <td>297.00977</td>\n",
       "      <td>47.121021</td>\n",
       "      <td>14.826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6991 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     koi_disposition  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  \\\n",
       "0          CONFIRMED              0              0              0   \n",
       "1     FALSE POSITIVE              0              1              0   \n",
       "2     FALSE POSITIVE              0              1              0   \n",
       "3          CONFIRMED              0              0              0   \n",
       "4          CONFIRMED              0              0              0   \n",
       "...              ...            ...            ...            ...   \n",
       "6986  FALSE POSITIVE              0              0              0   \n",
       "6987  FALSE POSITIVE              0              1              1   \n",
       "6988       CANDIDATE              0              0              0   \n",
       "6989  FALSE POSITIVE              0              0              1   \n",
       "6990  FALSE POSITIVE              0              0              1   \n",
       "\n",
       "      koi_fpflag_ec  koi_period  koi_period_err1  koi_period_err2  \\\n",
       "0                 0   54.418383     2.479000e-04    -2.479000e-04   \n",
       "1                 0   19.899140     1.490000e-05    -1.490000e-05   \n",
       "2                 0    1.736952     2.630000e-07    -2.630000e-07   \n",
       "3                 0    2.525592     3.760000e-06    -3.760000e-06   \n",
       "4                 0    4.134435     1.050000e-05    -1.050000e-05   \n",
       "...             ...         ...              ...              ...   \n",
       "6986              1    8.589871     1.846000e-04    -1.846000e-04   \n",
       "6987              0    0.527699     1.160000e-07    -1.160000e-07   \n",
       "6988              0    1.739849     1.780000e-05    -1.780000e-05   \n",
       "6989              0    0.681402     2.430000e-06    -2.430000e-06   \n",
       "6990              1    4.856035     6.360000e-05    -6.360000e-05   \n",
       "\n",
       "      koi_time0bk  koi_time0bk_err1  ...  koi_steff_err2  koi_slogg  \\\n",
       "0      162.513840          0.003520  ...             -81      4.467   \n",
       "1      175.850252          0.000581  ...            -176      4.544   \n",
       "2      170.307565          0.000115  ...            -174      4.564   \n",
       "3      171.595550          0.001130  ...            -211      4.438   \n",
       "4      172.979370          0.001900  ...            -232      4.486   \n",
       "...           ...               ...  ...             ...        ...   \n",
       "6986   132.016100          0.015700  ...            -152      4.296   \n",
       "6987   131.705093          0.000170  ...            -166      4.529   \n",
       "6988   133.001270          0.007690  ...            -220      4.444   \n",
       "6989   132.181750          0.002850  ...            -236      4.447   \n",
       "6990   135.993300          0.010800  ...            -225      4.385   \n",
       "\n",
       "      koi_slogg_err1  koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2  \\\n",
       "0              0.064          -0.096     0.927          0.105         -0.061   \n",
       "1              0.044          -0.176     0.868          0.233         -0.078   \n",
       "2              0.053          -0.168     0.791          0.201         -0.067   \n",
       "3              0.070          -0.210     1.046          0.334         -0.133   \n",
       "4              0.054          -0.229     0.972          0.315         -0.105   \n",
       "...              ...             ...       ...            ...            ...   \n",
       "6986           0.231          -0.189     1.088          0.313         -0.228   \n",
       "6987           0.035          -0.196     0.903          0.237         -0.079   \n",
       "6988           0.056          -0.224     1.031          0.341         -0.114   \n",
       "6989           0.056          -0.224     1.041          0.341         -0.114   \n",
       "6990           0.054          -0.216     1.193          0.410         -0.137   \n",
       "\n",
       "             ra        dec  koi_kepmag  \n",
       "0     291.93423  48.141651      15.347  \n",
       "1     297.00482  48.134129      15.436  \n",
       "2     285.53461  48.285210      15.597  \n",
       "3     288.75488  48.226200      15.509  \n",
       "4     296.28613  48.224670      15.714  \n",
       "...         ...        ...         ...  \n",
       "6986  298.74921  46.973351      14.478  \n",
       "6987  297.18875  47.093819      14.082  \n",
       "6988  286.50937  47.163219      14.757  \n",
       "6989  294.16489  47.176281      15.385  \n",
       "6990  297.00977  47.121021      14.826  \n",
       "\n",
       "[6991 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"exoplanet_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the null columns where all values are null\n",
    "data = df.dropna(axis='columns', how='all')\n",
    "# Drop the null rows\n",
    "data = df.dropna()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert dtypes of int64 to float64\n",
    "for column, content in data.items():\n",
    "    if data[column].dtype == 'int64':\n",
    "        data = data.astype({column: 'float64'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set features. This will also be used as your x values.  **NO COLUMNS WERE DELETED, WAS NOT SURE WHAT WAS GOING TO BE USED\n",
    "\n",
    "#selected_features = df[['names', 'of', 'selected', 'features', 'here']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `koi_disposition` for the y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign data to X and y\n",
    "X = data.drop(\"koi_disposition\", axis=1)\n",
    "y = data[\"koi_disposition\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and testing groups\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>6080</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.496435</td>\n",
       "      <td>2.213000e-04</td>\n",
       "      <td>-2.213000e-04</td>\n",
       "      <td>132.035800</td>\n",
       "      <td>0.014300</td>\n",
       "      <td>-0.014300</td>\n",
       "      <td>...</td>\n",
       "      <td>-286.0</td>\n",
       "      <td>3.805</td>\n",
       "      <td>0.390</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>2.730</td>\n",
       "      <td>0.535</td>\n",
       "      <td>-1.248</td>\n",
       "      <td>289.23080</td>\n",
       "      <td>44.412483</td>\n",
       "      <td>13.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.615625</td>\n",
       "      <td>1.528000e-04</td>\n",
       "      <td>-1.528000e-04</td>\n",
       "      <td>131.968430</td>\n",
       "      <td>0.008230</td>\n",
       "      <td>-0.008230</td>\n",
       "      <td>...</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>4.083</td>\n",
       "      <td>0.368</td>\n",
       "      <td>-0.092</td>\n",
       "      <td>1.453</td>\n",
       "      <td>0.218</td>\n",
       "      <td>-0.510</td>\n",
       "      <td>293.52756</td>\n",
       "      <td>41.111439</td>\n",
       "      <td>15.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.980246</td>\n",
       "      <td>6.930000e-07</td>\n",
       "      <td>-6.930000e-07</td>\n",
       "      <td>137.137607</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>-0.000053</td>\n",
       "      <td>...</td>\n",
       "      <td>-159.0</td>\n",
       "      <td>4.462</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.182</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.238</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>282.79764</td>\n",
       "      <td>43.578129</td>\n",
       "      <td>14.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>466.908240</td>\n",
       "      <td>1.194000e-02</td>\n",
       "      <td>-1.194000e-02</td>\n",
       "      <td>136.373100</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>-0.019000</td>\n",
       "      <td>...</td>\n",
       "      <td>-146.0</td>\n",
       "      <td>4.456</td>\n",
       "      <td>0.102</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>297.65436</td>\n",
       "      <td>43.178551</td>\n",
       "      <td>15.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.061933</td>\n",
       "      <td>1.250000e-06</td>\n",
       "      <td>-1.250000e-06</td>\n",
       "      <td>133.850441</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>-0.000978</td>\n",
       "      <td>...</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>3.975</td>\n",
       "      <td>0.259</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>1.851</td>\n",
       "      <td>0.383</td>\n",
       "      <td>-0.575</td>\n",
       "      <td>288.90253</td>\n",
       "      <td>44.632992</td>\n",
       "      <td>12.953</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",
       "6080            1.0            0.0            0.0            0.0   12.496435   \n",
       "3001            0.0            0.0            0.0            0.0   11.615625   \n",
       "570             0.0            1.0            0.0            0.0   10.980246   \n",
       "4897            1.0            0.0            0.0            0.0  466.908240   \n",
       "625             0.0            1.0            1.0            1.0    1.061933   \n",
       "\n",
       "      koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "6080     2.213000e-04    -2.213000e-04   132.035800          0.014300   \n",
       "3001     1.528000e-04    -1.528000e-04   131.968430          0.008230   \n",
       "570      6.930000e-07    -6.930000e-07   137.137607          0.000053   \n",
       "4897     1.194000e-02    -1.194000e-02   136.373100          0.019000   \n",
       "625      1.250000e-06    -1.250000e-06   133.850441          0.000978   \n",
       "\n",
       "      koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "6080         -0.014300  ...          -286.0      3.805           0.390   \n",
       "3001         -0.008230  ...           -72.0      4.083           0.368   \n",
       "570          -0.000053  ...          -159.0      4.462           0.098   \n",
       "4897         -0.019000  ...          -146.0      4.456           0.102   \n",
       "625          -0.000978  ...          -167.0      3.975           0.259   \n",
       "\n",
       "      koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "6080          -0.130     2.730          0.535         -1.248  289.23080   \n",
       "3001          -0.092     1.453          0.218         -0.510  293.52756   \n",
       "570           -0.182     0.897          0.238         -0.119  282.79764   \n",
       "4897          -0.361     0.867          0.448         -0.103  297.65436   \n",
       "625           -0.111     1.851          0.383         -0.575  288.90253   \n",
       "\n",
       "            dec  koi_kepmag  \n",
       "6080  44.412483      13.054  \n",
       "3001  41.111439      15.162  \n",
       "570   43.578129      14.212  \n",
       "4897  43.178551      15.202  \n",
       "625   44.632992      12.953  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5243, 40)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5243,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale data.Scale X values\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 100.0 %\n",
      "Testing Data Score: 89.817 %\n"
     ]
    }
   ],
   "source": [
    "#Train the model\n",
    "model_1 = RandomForestClassifier(n_estimators=200)\n",
    "model_1.fit(X_train_scaled, y_train)\n",
    "\n",
    "model_1_training_score = round(model_1.score(X_train_scaled, y_train)*100,3)\n",
    "base_accuracy = round(model_1.score(X_test_scaled, y_test)*100,3)\n",
    "\n",
    "print(f\"Training Data Score: {model_1_training_score} %\")\n",
    "print(f\"Testing Data Score: {base_accuracy} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <td>0.107541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_fpflag_nt</th>\n",
       "      <td>0.094018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <td>0.072829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_prad</th>\n",
       "      <td>0.053628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_model_snr</th>\n",
       "      <td>0.051331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Score\n",
       "Feature                \n",
       "koi_fpflag_co  0.107541\n",
       "koi_fpflag_nt  0.094018\n",
       "koi_fpflag_ss  0.072829\n",
       "koi_prad       0.053628\n",
       "koi_model_snr  0.051331"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select Features. Evaluate features and determine which features ought to be kept\n",
    "feature_names = X.columns.tolist()\n",
    "preSelected_features = sorted(zip(model_1.feature_importances_, feature_names), reverse=True)\n",
    "ranked_features = pd.DataFrame(preSelected_features, columns=['Score', 'Feature'])\n",
    "ranked_features = ranked_features.set_index('Feature')\n",
    "ranked_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.10754091889959869, 'koi_fpflag_co'),\n",
       " (0.09401797893517769, 'koi_fpflag_nt'),\n",
       " (0.07282862294747208, 'koi_fpflag_ss'),\n",
       " (0.05362828021359452, 'koi_prad'),\n",
       " (0.05133081879949411, 'koi_model_snr'),\n",
       " (0.034873190776529436, 'koi_fpflag_ec'),\n",
       " (0.0327581817753662, 'koi_duration_err1'),\n",
       " (0.032242008347641925, 'koi_steff_err1'),\n",
       " (0.031817261472780134, 'koi_duration_err2'),\n",
       " (0.03011982822100953, 'koi_prad_err1'),\n",
       " (0.028251130646539814, 'koi_prad_err2'),\n",
       " (0.027563159834911546, 'koi_steff_err2'),\n",
       " (0.025443968347453244, 'koi_time0bk_err2'),\n",
       " (0.025127514826591604, 'koi_time0bk_err1'),\n",
       " (0.02418467150841555, 'koi_duration'),\n",
       " (0.02130178759021711, 'koi_insol_err1'),\n",
       " (0.02095503969540438, 'koi_depth'),\n",
       " (0.020208960294848706, 'koi_period'),\n",
       " (0.01876295617294176, 'koi_period_err2'),\n",
       " (0.0183986108032228, 'koi_period_err1'),\n",
       " (0.01773176099291223, 'koi_impact'),\n",
       " (0.015407557259241507, 'koi_depth_err2'),\n",
       " (0.015311736702268824, 'koi_teq'),\n",
       " (0.014349463640498765, 'koi_time0bk'),\n",
       " (0.01304021760962464, 'ra'),\n",
       " (0.012827969619857134, 'koi_insol'),\n",
       " (0.012600778709959062, 'koi_depth_err1'),\n",
       " (0.01201394485171655, 'koi_insol_err2'),\n",
       " (0.01186115171096948, 'koi_srad_err1'),\n",
       " (0.011391755541049686, 'koi_kepmag'),\n",
       " (0.010698454697285564, 'dec'),\n",
       " (0.01051499292241414, 'koi_impact_err1'),\n",
       " (0.01050115004164637, 'koi_slogg_err2'),\n",
       " (0.010286907980023394, 'koi_impact_err2'),\n",
       " (0.009943822022988647, 'koi_slogg'),\n",
       " (0.009831119446275816, 'koi_steff'),\n",
       " (0.009147918748318211, 'koi_slogg_err1'),\n",
       " (0.009126475171787376, 'koi_srad'),\n",
       " (0.008667652197337726, 'koi_srad_err2'),\n",
       " (0.0033902800246140897, 'koi_tce_plnt_num')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preSelected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove features with Score < 0.011\n",
    "selected_features = []\n",
    "for tup in preSelected_features:\n",
    "    if tup[0] > 0.01:\n",
    "        selected_features.append(tup[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 100.0 %\n",
      "Testing Data Score: 90.16 %\n"
     ]
    }
   ],
   "source": [
    "# Use new data for all subsequent models. Assign new data to X \n",
    "X_train_select = X_train[selected_features]\n",
    "X_test_select = X_test[selected_features]\n",
    "\n",
    "X_scaler = MinMaxScaler().fit(X_train_select)\n",
    "X_train_scaled = X_scaler.transform(X_train_select)\n",
    "X_test_scaled = X_scaler.transform(X_test_select)\n",
    "\n",
    "## Train new model\n",
    "model_2 = RandomForestClassifier(n_estimators=200)\n",
    "model_2.fit(X_train_scaled, y_train)\n",
    "\n",
    "model_2_training_score = round(model_2.score(X_train_scaled, y_train)*100,3)\n",
    "select_features_accuracy = round(model_2.score(X_test_scaled, y_test)*100,3)\n",
    "\n",
    "print(f\"Training Data Score: {model_2_training_score} %\")\n",
    "print(f\"Testing Data Score: {select_features_accuracy} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning\n",
    "\n",
    "Use `GridSearchCV` to tune the model's parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:   34.9s\n",
      "[Parallel(n_jobs=-1)]: Done 112 tasks      | elapsed:  4.0min\n",
      "[Parallel(n_jobs=-1)]: Done 272 tasks      | elapsed:  9.5min\n",
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed: 12.6min finished\n"
     ]
    }
   ],
   "source": [
    "# Create the GridSearchCV model. RandomSearchCV model\n",
    "model_3 = RandomForestClassifier(random_state=42)\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [200, 600, 1200, 1400],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth': [14, 15, 16, 17, 18, None]\n",
    "}\n",
    "grid = GridSearchCV(model_3, param_grid, cv=5, verbose=3, n_jobs=-1)\n",
    "\n",
    "# Train the model with GridSearch\n",
    "_ = grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 99.39 %\n",
      "Testing Data Score: 89.703 %\n"
     ]
    }
   ],
   "source": [
    "# Tuned parameters\n",
    "max_features = grid.best_params_['max_features']\n",
    "n_estimators = grid.best_params_['n_estimators']\n",
    "max_depth = grid.best_params_['max_depth']\n",
    "criterion = 'entropy'\n",
    "\n",
    "# Tuned model\n",
    "tuned_model = RandomForestClassifier(max_features=max_features, n_estimators=n_estimators, \n",
    "                                     criterion=criterion, max_depth=max_depth, random_state=42)\n",
    "tuned_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "model_3_training_score = round(tuned_model.score(X_train_scaled, y_train)*100,3)\n",
    "tuned_accuracy = round(tuned_model.score(X_test_scaled, y_test)*100,3)\n",
    "\n",
    "print(f\"Training Data Score: {model_3_training_score} %\")\n",
    "print(f\"Testing Data Score: {tuned_accuracy} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8901381904976751\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL PREDICTIONS AND EVALUATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Actual</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>CONFIRMED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual      Prediction\n",
       "0        CANDIDATE       CANDIDATE\n",
       "1   FALSE POSITIVE  FALSE POSITIVE\n",
       "2   FALSE POSITIVE  FALSE POSITIVE\n",
       "3   FALSE POSITIVE  FALSE POSITIVE\n",
       "4        CANDIDATE       CANDIDATE\n",
       "5   FALSE POSITIVE  FALSE POSITIVE\n",
       "6        CANDIDATE       CANDIDATE\n",
       "7   FALSE POSITIVE  FALSE POSITIVE\n",
       "8   FALSE POSITIVE  FALSE POSITIVE\n",
       "9   FALSE POSITIVE  FALSE POSITIVE\n",
       "10  FALSE POSITIVE  FALSE POSITIVE\n",
       "11  FALSE POSITIVE  FALSE POSITIVE\n",
       "12       CONFIRMED       CONFIRMED\n",
       "13  FALSE POSITIVE  FALSE POSITIVE\n",
       "14  FALSE POSITIVE  FALSE POSITIVE"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PREDICTIONS \n",
    "predictions = tuned_model.predict(X_test_scaled)\n",
    "classifications = y_test.unique().tolist()\n",
    "\n",
    "prediction_actual = {\n",
    "    'Actual': y_test,\n",
    "    'Prediction': predictions\n",
    "}\n",
    "\n",
    "PA_df = pd.DataFrame(prediction_actual)\n",
    "PA_df = PA_df.set_index('Actual').reset_index()\n",
    "PA_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base Model</th>\n",
       "      <td>89.817%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Select Features Model</th>\n",
       "      <td>90.16%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Model</th>\n",
       "      <td>89.703%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Accuracy\n",
       "                              \n",
       "Base Model             89.817%\n",
       "Select Features Model   90.16%\n",
       "Tuned Model            89.703%"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#EVALUATIONS\n",
    "evaluations = {'': ['Base Model', 'Select Features Model', 'Tuned Model'],\n",
    "               'Accuracy': [f\"{base_accuracy}%\", f\"{select_features_accuracy}%\", f\"{tuned_accuracy}%\"]}\n",
    "\n",
    "evaluations_df = pd.DataFrame(evaluations)\n",
    "evaluations_df = evaluations_df.set_index('')\n",
    "\n",
    "evaluations_df.to_csv('output/RandomForestClassifier_evaluate.csv')\n",
    "evaluations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/EPRandomForest_evaluate.sav']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save your model by updating \"your_name\" with your name\n",
    "# and \"your_model\" with your model variable\n",
    "# be sure to turn this in to BCS\n",
    "# if joblib fails to import, try running the command to install in terminal/git-bash\n",
    "\n",
    "import joblib\n",
    "filename = ('models/EPRandomForest_evaluate.sav')\n",
    "joblib.dump(grid,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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