exoplanet_exploration_svm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Update sklearn to prevent version mismatches\n",
    "# !pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 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": 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",
       "      <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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in csv\n",
    "df = pd.read_csv(\"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": "code",
   "execution_count": 5,
   "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_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>count</th>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6.991000e+03</td>\n",
       "      <td>6.991000e+03</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.00000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.157059</td>\n",
       "      <td>0.244743</td>\n",
       "      <td>0.202975</td>\n",
       "      <td>0.125018</td>\n",
       "      <td>56.191248</td>\n",
       "      <td>1.851122e-03</td>\n",
       "      <td>-1.851122e-03</td>\n",
       "      <td>164.488820</td>\n",
       "      <td>0.009340</td>\n",
       "      <td>-0.009340</td>\n",
       "      <td>...</td>\n",
       "      <td>-161.206980</td>\n",
       "      <td>4.305049</td>\n",
       "      <td>0.121091</td>\n",
       "      <td>-0.14048</td>\n",
       "      <td>1.740749</td>\n",
       "      <td>0.352710</td>\n",
       "      <td>-0.388568</td>\n",
       "      <td>292.082406</td>\n",
       "      <td>43.812143</td>\n",
       "      <td>14.271508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.363882</td>\n",
       "      <td>0.429966</td>\n",
       "      <td>0.402243</td>\n",
       "      <td>0.330763</td>\n",
       "      <td>117.570962</td>\n",
       "      <td>7.184503e-03</td>\n",
       "      <td>7.184503e-03</td>\n",
       "      <td>67.020475</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>...</td>\n",
       "      <td>71.448481</td>\n",
       "      <td>0.439238</td>\n",
       "      <td>0.132048</td>\n",
       "      <td>0.08199</td>\n",
       "      <td>5.903415</td>\n",
       "      <td>0.839017</td>\n",
       "      <td>1.907797</td>\n",
       "      <td>4.762908</td>\n",
       "      <td>3.606167</td>\n",
       "      <td>1.350802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.259820</td>\n",
       "      <td>1.100000e-08</td>\n",
       "      <td>-1.568000e-01</td>\n",
       "      <td>120.515914</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-0.569000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1733.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.00700</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-103.825000</td>\n",
       "      <td>279.856080</td>\n",
       "      <td>36.577381</td>\n",
       "      <td>6.966000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.620126</td>\n",
       "      <td>5.005000e-06</td>\n",
       "      <td>-2.401000e-04</td>\n",
       "      <td>132.683917</td>\n",
       "      <td>0.001145</td>\n",
       "      <td>-0.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>-197.000000</td>\n",
       "      <td>4.209000</td>\n",
       "      <td>0.044000</td>\n",
       "      <td>-0.19500</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>-0.252000</td>\n",
       "      <td>288.704730</td>\n",
       "      <td>40.797760</td>\n",
       "      <td>13.455000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.947426</td>\n",
       "      <td>3.300000e-05</td>\n",
       "      <td>-3.300000e-05</td>\n",
       "      <td>136.739230</td>\n",
       "      <td>0.003990</td>\n",
       "      <td>-0.003990</td>\n",
       "      <td>...</td>\n",
       "      <td>-159.000000</td>\n",
       "      <td>4.436000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>-0.12700</td>\n",
       "      <td>0.999000</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>-0.111000</td>\n",
       "      <td>292.314760</td>\n",
       "      <td>43.679661</td>\n",
       "      <td>14.534000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.282605</td>\n",
       "      <td>2.401000e-04</td>\n",
       "      <td>-5.005000e-06</td>\n",
       "      <td>169.937005</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-0.001145</td>\n",
       "      <td>...</td>\n",
       "      <td>-112.000000</td>\n",
       "      <td>4.543000</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>-0.08800</td>\n",
       "      <td>1.357000</td>\n",
       "      <td>0.357000</td>\n",
       "      <td>-0.069000</td>\n",
       "      <td>295.888550</td>\n",
       "      <td>46.693659</td>\n",
       "      <td>15.322000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1071.232624</td>\n",
       "      <td>1.568000e-01</td>\n",
       "      <td>-1.100000e-08</td>\n",
       "      <td>1472.522306</td>\n",
       "      <td>0.569000</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.364000</td>\n",
       "      <td>1.472000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>180.013000</td>\n",
       "      <td>25.956000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>301.720760</td>\n",
       "      <td>52.336010</td>\n",
       "      <td>19.065000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  \\\n",
       "count    6991.000000    6991.000000    6991.000000    6991.000000   \n",
       "mean        0.157059       0.244743       0.202975       0.125018   \n",
       "std         0.363882       0.429966       0.402243       0.330763   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "        koi_period  koi_period_err1  koi_period_err2  koi_time0bk  \\\n",
       "count  6991.000000     6.991000e+03     6.991000e+03  6991.000000   \n",
       "mean     56.191248     1.851122e-03    -1.851122e-03   164.488820   \n",
       "std     117.570962     7.184503e-03     7.184503e-03    67.020475   \n",
       "min       0.259820     1.100000e-08    -1.568000e-01   120.515914   \n",
       "25%       2.620126     5.005000e-06    -2.401000e-04   132.683917   \n",
       "50%       8.947426     3.300000e-05    -3.300000e-05   136.739230   \n",
       "75%      34.282605     2.401000e-04    -5.005000e-06   169.937005   \n",
       "max    1071.232624     1.568000e-01    -1.100000e-08  1472.522306   \n",
       "\n",
       "       koi_time0bk_err1  koi_time0bk_err2  ...  koi_steff_err2    koi_slogg  \\\n",
       "count       6991.000000       6991.000000  ...     6991.000000  6991.000000   \n",
       "mean           0.009340         -0.009340  ...     -161.206980     4.305049   \n",
       "std            0.021989          0.021989  ...       71.448481     0.439238   \n",
       "min            0.000009         -0.569000  ...    -1733.000000     0.047000   \n",
       "25%            0.001145         -0.010000  ...     -197.000000     4.209000   \n",
       "50%            0.003990         -0.003990  ...     -159.000000     4.436000   \n",
       "75%            0.010000         -0.001145  ...     -112.000000     4.543000   \n",
       "max            0.569000         -0.000009  ...        0.000000     5.364000   \n",
       "\n",
       "       koi_slogg_err1  koi_slogg_err2     koi_srad  koi_srad_err1  \\\n",
       "count     6991.000000      6991.00000  6991.000000    6991.000000   \n",
       "mean         0.121091        -0.14048     1.740749       0.352710   \n",
       "std          0.132048         0.08199     5.903415       0.839017   \n",
       "min          0.000000        -1.00700     0.109000       0.000000   \n",
       "25%          0.044000        -0.19500     0.829000       0.128000   \n",
       "50%          0.070000        -0.12700     0.999000       0.248000   \n",
       "75%          0.149000        -0.08800     1.357000       0.357000   \n",
       "max          1.472000         0.00000   180.013000      25.956000   \n",
       "\n",
       "       koi_srad_err2           ra          dec   koi_kepmag  \n",
       "count    6991.000000  6991.000000  6991.000000  6991.000000  \n",
       "mean       -0.388568   292.082406    43.812143    14.271508  \n",
       "std         1.907797     4.762908     3.606167     1.350802  \n",
       "min      -103.825000   279.856080    36.577381     6.966000  \n",
       "25%        -0.252000   288.704730    40.797760    13.455000  \n",
       "50%        -0.111000   292.314760    43.679661    14.534000  \n",
       "75%        -0.069000   295.888550    46.693659    15.322000  \n",
       "max         0.000000   301.720760    52.336010    19.065000  \n",
       "\n",
       "[8 rows x 40 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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_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>0</th>\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>-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>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>-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>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>-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>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>-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>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>-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 × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  koi_period  \\\n",
       "0              0              0              0              0   54.418383   \n",
       "1              0              1              0              0   19.899140   \n",
       "2              0              1              0              0    1.736952   \n",
       "3              0              0              0              0    2.525592   \n",
       "4              0              0              0              0    4.134435   \n",
       "\n",
       "   koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "0     2.479000e-04    -2.479000e-04   162.513840          0.003520   \n",
       "1     1.490000e-05    -1.490000e-05   175.850252          0.000581   \n",
       "2     2.630000e-07    -2.630000e-07   170.307565          0.000115   \n",
       "3     3.760000e-06    -3.760000e-06   171.595550          0.001130   \n",
       "4     1.050000e-05    -1.050000e-05   172.979370          0.001900   \n",
       "\n",
       "   koi_time0bk_err2  ...  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 40 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set target, features and feature_names.\n",
    "target = df[\"koi_disposition\"]\n",
    "data = df.drop(\"koi_disposition\", axis=1)\n",
    "feature_names = data.columns\n",
    "data.head()"
   ]
  },
  {
   "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": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6122</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.768901</td>\n",
       "      <td>7.380000e-05</td>\n",
       "      <td>-7.380000e-05</td>\n",
       "      <td>133.077240</td>\n",
       "      <td>0.008440</td>\n",
       "      <td>-0.008440</td>\n",
       "      <td>...</td>\n",
       "      <td>-171</td>\n",
       "      <td>4.327</td>\n",
       "      <td>0.153</td>\n",
       "      <td>-0.187</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.310</td>\n",
       "      <td>-0.207</td>\n",
       "      <td>294.40472</td>\n",
       "      <td>39.351681</td>\n",
       "      <td>14.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.733726</td>\n",
       "      <td>6.060000e-06</td>\n",
       "      <td>-6.060000e-06</td>\n",
       "      <td>132.020050</td>\n",
       "      <td>0.007950</td>\n",
       "      <td>-0.007950</td>\n",
       "      <td>...</td>\n",
       "      <td>-175</td>\n",
       "      <td>4.578</td>\n",
       "      <td>0.033</td>\n",
       "      <td>-0.187</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.211</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>284.50391</td>\n",
       "      <td>42.463860</td>\n",
       "      <td>15.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.652707</td>\n",
       "      <td>6.540000e-05</td>\n",
       "      <td>-6.540000e-05</td>\n",
       "      <td>134.460380</td>\n",
       "      <td>0.006190</td>\n",
       "      <td>-0.006190</td>\n",
       "      <td>...</td>\n",
       "      <td>-189</td>\n",
       "      <td>4.481</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.290</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>295.50211</td>\n",
       "      <td>38.983540</td>\n",
       "      <td>13.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.953547</td>\n",
       "      <td>1.910000e-05</td>\n",
       "      <td>-1.910000e-05</td>\n",
       "      <td>174.662240</td>\n",
       "      <td>0.001820</td>\n",
       "      <td>-0.001820</td>\n",
       "      <td>...</td>\n",
       "      <td>-85</td>\n",
       "      <td>4.536</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.779</td>\n",
       "      <td>0.023</td>\n",
       "      <td>-0.049</td>\n",
       "      <td>291.15878</td>\n",
       "      <td>40.750271</td>\n",
       "      <td>15.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.959319</td>\n",
       "      <td>5.150000e-07</td>\n",
       "      <td>-5.150000e-07</td>\n",
       "      <td>172.258529</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>...</td>\n",
       "      <td>-77</td>\n",
       "      <td>4.359</td>\n",
       "      <td>0.110</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>1.082</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>292.16705</td>\n",
       "      <td>48.727589</td>\n",
       "      <td>15.263</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",
       "6122              0              0              0              0    6.768901   \n",
       "6370              0              1              0              1    0.733726   \n",
       "2879              1              0              0              0    7.652707   \n",
       "107               0              0              0              0    7.953547   \n",
       "29                0              0              0              0    4.959319   \n",
       "\n",
       "      koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "6122     7.380000e-05    -7.380000e-05   133.077240          0.008440   \n",
       "6370     6.060000e-06    -6.060000e-06   132.020050          0.007950   \n",
       "2879     6.540000e-05    -6.540000e-05   134.460380          0.006190   \n",
       "107      1.910000e-05    -1.910000e-05   174.662240          0.001820   \n",
       "29       5.150000e-07    -5.150000e-07   172.258529          0.000083   \n",
       "\n",
       "      koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "6122         -0.008440  ...            -171      4.327           0.153   \n",
       "6370         -0.007950  ...            -175      4.578           0.033   \n",
       "2879         -0.006190  ...            -189      4.481           0.050   \n",
       "107          -0.001820  ...             -85      4.536           0.056   \n",
       "29           -0.000083  ...             -77      4.359           0.110   \n",
       "\n",
       "      koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "6122          -0.187     1.125          0.310         -0.207  294.40472   \n",
       "6370          -0.187     0.797          0.211         -0.056  284.50391   \n",
       "2879          -0.200     0.963          0.290         -0.097  295.50211   \n",
       "107           -0.016     0.779          0.023         -0.049  291.15878   \n",
       "29            -0.110     1.082          0.173         -0.130  292.16705   \n",
       "\n",
       "            dec  koi_kepmag  \n",
       "6122  39.351681      14.725  \n",
       "6370  42.463860      15.770  \n",
       "2879  38.983540      13.099  \n",
       "107   40.750271      15.660  \n",
       "29    48.727589      15.263  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "X_minmax = MinMaxScaler().fit(X_train)\n",
    "\n",
    "X_train_minmax = X_minmax.transform(X_train)\n",
    "X_test_minmax = X_minmax.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC \n",
    "model = SVC(kernel='linear')\n",
    "model.fit(X_train_minmax, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.8455082967766546\n",
      "Testing Data Score: 0.8415331807780321\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score: {model.score(X_train_minmax, y_train)}\")\n",
    "print(f\"Testing Data Score: {model.score(X_test_minmax, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning\n",
    "\n",
    "Use `GridSearchCV` to tune the model's parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the GridSearchCV model\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [1, 5, 10, 50],\n",
    "              'gamma': [0.0001, 0.0005, 0.001, 0.005]}\n",
    "grid = GridSearchCV(model, param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=1, gamma=0.0001, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=1, gamma=0.0001, score=0.825, total=   0.3s\n",
      "[CV] C=1, gamma=0.0005 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0005, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.0005 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0005, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.0005 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0005, score=0.825, total=   0.3s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.825, total=   0.3s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.847, total=   0.3s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.825, total=   0.3s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.864, total=   0.3s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.867, total=   0.3s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.853, total=   0.3s\n",
      "[CV] C=5, gamma=0.0005 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0005, score=0.864, total=   0.3s\n",
      "[CV] C=5, gamma=0.0005 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0005, score=0.867, total=   0.3s\n",
      "[CV] C=5, gamma=0.0005 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0005, score=0.853, total=   0.3s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.864, total=   0.3s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.867, total=   0.3s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.853, total=   0.3s\n",
      "[CV] C=5, gamma=0.005 ................................................\n",
      "[CV] .................... C=5, gamma=0.005, score=0.864, total=   0.3s\n",
      "[CV] C=5, gamma=0.005 ................................................\n",
      "[CV] .................... C=5, gamma=0.005, score=0.867, total=   0.3s\n",
      "[CV] C=5, gamma=0.005 ................................................\n",
      "[CV] .................... C=5, gamma=0.005, score=0.853, total=   0.3s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.878, total=   0.3s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.876, total=   0.3s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.856, total=   0.3s\n",
      "[CV] C=10, gamma=0.0005 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0005, score=0.878, total=   0.3s\n",
      "[CV] C=10, gamma=0.0005 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0005, score=0.876, total=   0.3s\n",
      "[CV] C=10, gamma=0.0005 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0005, score=0.856, total=   0.3s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.878, total=   0.4s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.876, total=   0.3s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.856, total=   0.3s\n",
      "[CV] C=10, gamma=0.005 ...............................................\n",
      "[CV] ................... C=10, gamma=0.005, score=0.878, total=   0.3s\n",
      "[CV] C=10, gamma=0.005 ...............................................\n",
      "[CV] ................... C=10, gamma=0.005, score=0.876, total=   0.3s\n",
      "[CV] C=10, gamma=0.005 ...............................................\n",
      "[CV] ................... C=10, gamma=0.005, score=0.856, total=   0.3s\n",
      "[CV] C=50, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=50, gamma=0.0001, score=0.891, total=   0.3s\n",
      "[CV] C=50, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=50, gamma=0.0001, score=0.887, total=   0.4s\n",
      "[CV] C=50, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=50, gamma=0.0001, score=0.866, total=   0.3s\n",
      "[CV] C=50, gamma=0.0005 ..............................................\n",
      "[CV] .................. C=50, gamma=0.0005, score=0.891, total=   0.3s\n",
      "[CV] C=50, gamma=0.0005 ..............................................\n",
      "[CV] .................. C=50, gamma=0.0005, score=0.887, total=   0.3s\n",
      "[CV] C=50, gamma=0.0005 ..............................................\n",
      "[CV] .................. C=50, gamma=0.0005, score=0.866, total=   0.3s\n",
      "[CV] C=50, gamma=0.001 ...............................................\n",
      "[CV] ................... C=50, gamma=0.001, score=0.891, total=   0.3s\n",
      "[CV] C=50, gamma=0.001 ...............................................\n",
      "[CV] ................... C=50, gamma=0.001, score=0.887, total=   0.3s\n",
      "[CV] C=50, gamma=0.001 ...............................................\n",
      "[CV] ................... C=50, gamma=0.001, score=0.866, total=   0.3s\n",
      "[CV] C=50, gamma=0.005 ...............................................\n",
      "[CV] ................... C=50, gamma=0.005, score=0.891, total=   0.3s\n",
      "[CV] C=50, gamma=0.005 ...............................................\n",
      "[CV] ................... C=50, gamma=0.005, score=0.887, total=   0.3s\n",
      "[CV] C=50, gamma=0.005 ...............................................\n",
      "[CV] ................... C=50, gamma=0.005, score=0.866, total=   0.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:   14.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='auto_deprecated', kernel='linear',\n",
       "                           max_iter=-1, probability=False, random_state=None,\n",
       "                           shrinking=True, tol=0.001, verbose=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': [1, 5, 10, 50],\n",
       "                         'gamma': [0.0001, 0.0005, 0.001, 0.005]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model with GridSearch\n",
    "grid.fit(X_train_minmax, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 50, 'gamma': 0.0001}\n",
      "0.8815563608621019\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8865153538050734"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training score:\n",
    "grid.score(X_train_minmax, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8792906178489702"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing score:\n",
    "grid.score(X_test_minmax, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make prediction and save to variable for report.\n",
    "predictions = grid.predict(X_test_minmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "     CANDIDATE       0.81      0.67      0.73       411\n",
      "     CONFIRMED       0.76      0.85      0.80       484\n",
      "FALSE POSITIVE       0.98      1.00      0.99       853\n",
      "\n",
      "      accuracy                           0.88      1748\n",
      "     macro avg       0.85      0.84      0.84      1748\n",
      "  weighted avg       0.88      0.88      0.88      1748\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print Classification Report.\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Models/zGrinacoff_svm.sav']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "filename = 'Models/zGrinacoff_svm.sav'\n",
    "joblib.dump(model, filename)"
   ]
  }
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