model_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 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",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"exoplanet_data.csv\")\n",
    "\n",
    "# Drop the null columns where all values are null\n",
    "df = df.dropna(axis='columns', how='all')\n",
    "\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set features. This will also be used as your x values.\n",
    "selected_features = df[['koi_fpflag_nt', 'koi_fpflag_ss', 'koi_fpflag_co', 'koi_fpflag_ec', 'koi_prad']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `koi_disposition` for the y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.koi_disposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use train_test_split to create training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(selected_features, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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_prad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4099</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5460</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  koi_prad\n",
       "3563              0              0              0              0      3.89\n",
       "4099              0              0              0              0      2.10\n",
       "5460              0              0              0              0     14.59\n",
       "1091              0              0              0              0      2.28\n",
       "5999              0              0              0              0      2.27"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale your data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "X_minmax = MinMaxScaler().fit(X_train)\n",
    "\n",
    "\n",
    "X_train_minmax = X_minmax.transform(X_train)\n",
    "X_test_minmax = X_minmax.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.87176316e-05],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        9.78308212e-06],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        7.21252738e-05],\n",
       "       ...,\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00,\n",
       "        1.42703223e-04],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.45248821e-05],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        8.68498106e-06]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support vector machine classifier\n",
    "from sklearn.svm import SVC \n",
    "model = SVC(kernel='rbf')\n",
    "model.fit(X_train_minmax, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [1, 5, 10],\n",
    "              'gamma': [0.0001, 0.001, 0.01]}\n",
    "grid = GridSearchCV(model, param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=1, gamma=0.0001, score=0.495, total=   1.1s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    1.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=1, gamma=0.0001, score=0.495, total=   1.2s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    2.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................... C=1, gamma=0.0001, score=0.495, total=   1.8s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.495, total=   1.3s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.495, total=   1.2s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.630, total=   1.2s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.634, total=   1.2s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.642, total=   1.2s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.631, total=   1.1s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.616, total=   1.3s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.741, total=   1.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.772, total=   0.9s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.773, total=   1.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.775, total=   0.9s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ..................... C=1, gamma=0.01, score=0.779, total=   0.9s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.672, total=   1.6s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.733, total=   1.3s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.726, total=   1.3s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.720, total=   1.2s\n",
      "[CV] C=5, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=5, gamma=0.0001, score=0.620, total=   1.1s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.724, total=   1.2s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.739, total=   1.1s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.730, total=   1.1s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.733, total=   1.3s\n",
      "[CV] C=5, gamma=0.001 ................................................\n",
      "[CV] .................... C=5, gamma=0.001, score=0.732, total=   1.1s\n",
      "[CV] C=5, gamma=0.01 .................................................\n",
      "[CV] ..................... C=5, gamma=0.01, score=0.759, total=   1.1s\n",
      "[CV] C=5, gamma=0.01 .................................................\n",
      "[CV] ..................... C=5, gamma=0.01, score=0.794, total=   0.9s\n",
      "[CV] C=5, gamma=0.01 .................................................\n",
      "[CV] ..................... C=5, gamma=0.01, score=0.784, total=   1.0s\n",
      "[CV] C=5, gamma=0.01 .................................................\n",
      "[CV] ..................... C=5, gamma=0.01, score=0.793, total=   0.9s\n",
      "[CV] C=5, gamma=0.01 .................................................\n",
      "[CV] ..................... C=5, gamma=0.01, score=0.797, total=   0.8s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.609, total=   1.2s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.627, total=   1.6s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.640, total=   1.4s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.627, total=   1.4s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] .................. C=10, gamma=0.0001, score=0.628, total=   1.4s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.743, total=   1.2s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.756, total=   1.1s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.743, total=   1.4s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.749, total=   1.3s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ................... C=10, gamma=0.001, score=0.746, total=   1.3s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=0.769, total=   0.7s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=0.795, total=   1.5s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=0.792, total=   1.3s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=0.797, total=   1.2s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] .................... C=10, gamma=0.01, score=0.801, total=   1.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:   53.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [1, 5, 10], 'gamma': [0.0001, 0.001, 0.01]},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model with GridSearch\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.01}\n",
      "0.7907716545746949\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['edie_svc_rbf.sav']"
      ]
     },
     "execution_count": 12,
     "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",
    "import joblib\n",
    "filename = 'edie_svc_rbf.sav'\n",
    "joblib.dump(grid, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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