machine-learning-challenge.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 pandas as pd\n",
    "from sklearn.svm import SVC "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "      <td>0</td>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "      <td>1</td>\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",
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       "      <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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>koi_fpflag_nt</th>\n",
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       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
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       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>19.899140</td>\n",
       "      <td>1.490000e-05</td>\n",
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       "      <td>0.000581</td>\n",
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       "      <td>4.544</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "      <td>15.509</td>\n",
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       "      <td>0.001900</td>\n",
       "      <td>-0.001900</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>6986</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>-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",
       "      <td>6987</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>-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",
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       "      <td>6988</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.739849</td>\n",
       "      <td>1.780000e-05</td>\n",
       "      <td>-1.780000e-05</td>\n",
       "      <td>133.001270</td>\n",
       "      <td>0.007690</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",
       "      <td>6989</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>-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",
       "      <td>6990</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>-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 × 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",
       "6986              0              0              0              1    8.589871   \n",
       "6987              0              1              1              0    0.527699   \n",
       "6988              0              0              0              0    1.739849   \n",
       "6989              0              0              1              0    0.681402   \n",
       "6990              0              0              1              1    4.856035   \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",
       "6986     1.846000e-04    -1.846000e-04   132.016100          0.015700   \n",
       "6987     1.160000e-07    -1.160000e-07   131.705093          0.000170   \n",
       "6988     1.780000e-05    -1.780000e-05   133.001270          0.007690   \n",
       "6989     2.430000e-06    -2.430000e-06   132.181750          0.002850   \n",
       "6990     6.360000e-05    -6.360000e-05   135.993300          0.010800   \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",
       "6986         -0.015700  ...            -152      4.296           0.231   \n",
       "6987         -0.000170  ...            -166      4.529           0.035   \n",
       "6988         -0.007690  ...            -220      4.444           0.056   \n",
       "6989         -0.002850  ...            -236      4.447           0.056   \n",
       "6990         -0.010800  ...            -225      4.385           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",
       "6986          -0.189     1.088          0.313         -0.228  298.74921   \n",
       "6987          -0.196     0.903          0.237         -0.079  297.18875   \n",
       "6988          -0.224     1.031          0.341         -0.114  286.50937   \n",
       "6989          -0.224     1.041          0.341         -0.114  294.16489   \n",
       "6990          -0.216     1.193          0.410         -0.137  297.00977   \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",
       "6986  46.973351      14.478  \n",
       "6987  47.093819      14.082  \n",
       "6988  47.163219      14.757  \n",
       "6989  47.176281      15.385  \n",
       "6990  47.121021      14.826  \n",
       "\n",
       "[6991 rows x 40 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set features. This will also be used as your x values.\n",
    "y=df['koi_disposition']\n",
    "X = df.drop(columns=['koi_disposition'])\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `koi_disposition` for the y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and testing; dividing data between training and testing (standard is an 80/20 split)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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",
       "      <td>6122</td>\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",
       "      <td>6370</td>\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",
       "      <td>2879</td>\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",
       "      <td>107</td>\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",
       "      <td>29</td>\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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale your data\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scale = scaler.transform(X_train)\n",
    "X_test_scale = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit to the training data and validate with the test data\n",
    "model = SVC(kernel='linear')\n",
    "model.fit(X_train_scale, y_train)\n",
    "# predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_scale, y_train)}\")\n",
    "print(f\"Testing Data Score: {model.score(X_test_scale, y_test)}\")"
   ]
  }
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