Exoplanet_Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6e672c9f-c495-4c37-b79e-96c75b4c816b",
   "metadata": {},
   "source": [
    "# Exoplanet_Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "4c3ad974-37dc-4d0c-a4be-06f551edc57d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   rowid     kepid kepoi_name   kepler_name koi_disposition koi_pdisposition  \\\n",
      "0      1  10797460  K00752.01  Kepler-227 b       CONFIRMED        CANDIDATE   \n",
      "1      2  10797460  K00752.02  Kepler-227 c       CONFIRMED        CANDIDATE   \n",
      "2      3  10811496  K00753.01           NaN  FALSE POSITIVE   FALSE POSITIVE   \n",
      "3      4  10848459  K00754.01           NaN  FALSE POSITIVE   FALSE POSITIVE   \n",
      "4      5  10854555  K00755.01  Kepler-664 b       CONFIRMED        CANDIDATE   \n",
      "\n",
      "   koi_score  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  ...  \\\n",
      "0      1.000              0              0              0  ...   \n",
      "1      0.969              0              0              0  ...   \n",
      "2      0.000              0              1              0  ...   \n",
      "3      0.000              0              1              0  ...   \n",
      "4      1.000              0              0              0  ...   \n",
      "\n",
      "   koi_steff_err2  koi_slogg  koi_slogg_err1  koi_slogg_err2  koi_srad  \\\n",
      "0           -81.0      4.467           0.064          -0.096     0.927   \n",
      "1           -81.0      4.467           0.064          -0.096     0.927   \n",
      "2          -176.0      4.544           0.044          -0.176     0.868   \n",
      "3          -174.0      4.564           0.053          -0.168     0.791   \n",
      "4          -211.0      4.438           0.070          -0.210     1.046   \n",
      "\n",
      "   koi_srad_err1  koi_srad_err2         ra        dec  koi_kepmag  \n",
      "0          0.105         -0.061  291.93423  48.141651      15.347  \n",
      "1          0.105         -0.061  291.93423  48.141651      15.347  \n",
      "2          0.233         -0.078  297.00482  48.134129      15.436  \n",
      "3          0.201         -0.067  285.53461  48.285210      15.597  \n",
      "4          0.334         -0.133  288.75488  48.226200      15.509  \n",
      "\n",
      "[5 rows x 50 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('Kepler_Data.csv')\n",
    "print(data.head()) #display the first few rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "3567a955-ef95-4304-903e-481c403ebde3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9564 entries, 0 to 9563\n",
      "Data columns (total 50 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   rowid              9564 non-null   int64  \n",
      " 1   kepid              9564 non-null   int64  \n",
      " 2   kepoi_name         9564 non-null   object \n",
      " 3   kepler_name        2294 non-null   object \n",
      " 4   koi_disposition    9564 non-null   object \n",
      " 5   koi_pdisposition   9564 non-null   object \n",
      " 6   koi_score          8054 non-null   float64\n",
      " 7   koi_fpflag_nt      9564 non-null   int64  \n",
      " 8   koi_fpflag_ss      9564 non-null   int64  \n",
      " 9   koi_fpflag_co      9564 non-null   int64  \n",
      " 10  koi_fpflag_ec      9564 non-null   int64  \n",
      " 11  koi_period         9564 non-null   float64\n",
      " 12  koi_period_err1    9110 non-null   float64\n",
      " 13  koi_period_err2    9110 non-null   float64\n",
      " 14  koi_time0bk        9564 non-null   float64\n",
      " 15  koi_time0bk_err1   9110 non-null   float64\n",
      " 16  koi_time0bk_err2   9110 non-null   float64\n",
      " 17  koi_impact         9201 non-null   float64\n",
      " 18  koi_impact_err1    9110 non-null   float64\n",
      " 19  koi_impact_err2    9110 non-null   float64\n",
      " 20  koi_duration       9564 non-null   float64\n",
      " 21  koi_duration_err1  9110 non-null   float64\n",
      " 22  koi_duration_err2  9110 non-null   float64\n",
      " 23  koi_depth          9201 non-null   float64\n",
      " 24  koi_depth_err1     9110 non-null   float64\n",
      " 25  koi_depth_err2     9110 non-null   float64\n",
      " 26  koi_prad           9201 non-null   float64\n",
      " 27  koi_prad_err1      9201 non-null   float64\n",
      " 28  koi_prad_err2      9201 non-null   float64\n",
      " 29  koi_teq            9201 non-null   float64\n",
      " 30  koi_teq_err1       0 non-null      float64\n",
      " 31  koi_teq_err2       0 non-null      float64\n",
      " 32  koi_insol          9243 non-null   float64\n",
      " 33  koi_insol_err1     9243 non-null   float64\n",
      " 34  koi_insol_err2     9243 non-null   float64\n",
      " 35  koi_model_snr      9201 non-null   float64\n",
      " 36  koi_tce_plnt_num   9218 non-null   float64\n",
      " 37  koi_tce_delivname  9218 non-null   object \n",
      " 38  koi_steff          9201 non-null   float64\n",
      " 39  koi_steff_err1     9096 non-null   float64\n",
      " 40  koi_steff_err2     9081 non-null   float64\n",
      " 41  koi_slogg          9201 non-null   float64\n",
      " 42  koi_slogg_err1     9096 non-null   float64\n",
      " 43  koi_slogg_err2     9096 non-null   float64\n",
      " 44  koi_srad           9201 non-null   float64\n",
      " 45  koi_srad_err1      9096 non-null   float64\n",
      " 46  koi_srad_err2      9096 non-null   float64\n",
      " 47  ra                 9564 non-null   float64\n",
      " 48  dec                9564 non-null   float64\n",
      " 49  koi_kepmag         9563 non-null   float64\n",
      "dtypes: float64(39), int64(6), object(5)\n",
      "memory usage: 3.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "55e078e8-69cf-4dc1-b658-608fbd0acd1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rowid                   0\n",
      "kepid                   0\n",
      "kepoi_name              0\n",
      "kepler_name          7270\n",
      "koi_disposition         0\n",
      "koi_pdisposition        0\n",
      "koi_score            1510\n",
      "koi_fpflag_nt           0\n",
      "koi_fpflag_ss           0\n",
      "koi_fpflag_co           0\n",
      "koi_fpflag_ec           0\n",
      "koi_period              0\n",
      "koi_period_err1       454\n",
      "koi_period_err2       454\n",
      "koi_time0bk             0\n",
      "koi_time0bk_err1      454\n",
      "koi_time0bk_err2      454\n",
      "koi_impact            363\n",
      "koi_impact_err1       454\n",
      "koi_impact_err2       454\n",
      "koi_duration            0\n",
      "koi_duration_err1     454\n",
      "koi_duration_err2     454\n",
      "koi_depth             363\n",
      "koi_depth_err1        454\n",
      "koi_depth_err2        454\n",
      "koi_prad              363\n",
      "koi_prad_err1         363\n",
      "koi_prad_err2         363\n",
      "koi_teq               363\n",
      "koi_teq_err1         9564\n",
      "koi_teq_err2         9564\n",
      "koi_insol             321\n",
      "koi_insol_err1        321\n",
      "koi_insol_err2        321\n",
      "koi_model_snr         363\n",
      "koi_tce_plnt_num      346\n",
      "koi_tce_delivname     346\n",
      "koi_steff             363\n",
      "koi_steff_err1        468\n",
      "koi_steff_err2        483\n",
      "koi_slogg             363\n",
      "koi_slogg_err1        468\n",
      "koi_slogg_err2        468\n",
      "koi_srad              363\n",
      "koi_srad_err1         468\n",
      "koi_srad_err2         468\n",
      "ra                      0\n",
      "dec                     0\n",
      "koi_kepmag              1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "1f654eb5-5400-4d2c-8c5b-21f94212b0a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['rowid', 'kepid', 'kepoi_name', 'kepler_name', 'koi_disposition',\n",
      "       'koi_pdisposition', 'koi_score', 'koi_fpflag_nt', 'koi_fpflag_ss',\n",
      "       'koi_fpflag_co', 'koi_fpflag_ec', 'koi_period', 'koi_period_err1',\n",
      "       'koi_period_err2', 'koi_time0bk', 'koi_time0bk_err1',\n",
      "       'koi_time0bk_err2', 'koi_impact', 'koi_impact_err1', 'koi_impact_err2',\n",
      "       'koi_duration', 'koi_duration_err1', 'koi_duration_err2', 'koi_depth',\n",
      "       'koi_depth_err1', 'koi_depth_err2', 'koi_prad', 'koi_prad_err1',\n",
      "       'koi_prad_err2', 'koi_teq', 'koi_teq_err1', 'koi_teq_err2', 'koi_insol',\n",
      "       'koi_insol_err1', 'koi_insol_err2', 'koi_model_snr', 'koi_tce_plnt_num',\n",
      "       'koi_tce_delivname', 'koi_steff', 'koi_steff_err1', 'koi_steff_err2',\n",
      "       'koi_slogg', 'koi_slogg_err1', 'koi_slogg_err2', 'koi_srad',\n",
      "       'koi_srad_err1', 'koi_srad_err2', 'ra', 'dec', 'koi_kepmag'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6ea385ba-d6c4-4253-83b0-f7dec3e3e07b",
   "metadata": {},
   "outputs": [],
   "source": [
    "unrelated_features = ['rowid', 'kepid', 'kepoi_name', 'kepler_name', 'koi_disposition', \n",
    "                      'koi_insol', 'koi_insol_err1', 'koi_insol_err2', 'koi_slogg', \n",
    "                       'koi_slogg_err1', 'koi_slogg_err2', 'koi_fpflag_ec',\n",
    "                      'koi_teq', 'koi_teq_err1', 'koi_teq_err2', 'koi_tce_delivname']\n",
    "data = data.drop(unrelated_features, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f9a45b14-192a-49af-8fc2-f74d9aa7279c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "bee5d65b-2502-4fae-a762-c478c340ccf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c3938b4d-09bb-4276-8aea-bb4a5796e177",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "9962c1d5-b23b-425c-ab91-b3642059d8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['koi_pdisposition'] = le.fit_transform(data['koi_pdisposition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b2175cfc-8b6c-42b5-9e29-f556f199ecfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_features = data.drop(['koi_pdisposition'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "9c3954fa-8fa5-41da-b850-f73e55282fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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_score</th>\n",
       "      <th>koi_fpflag_nt</th>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</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_tce_plnt_num</th>\n",
       "      <th>koi_steff</th>\n",
       "      <th>koi_steff_err1</th>\n",
       "      <th>koi_steff_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>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.488036</td>\n",
       "      <td>2.775000e-05</td>\n",
       "      <td>-2.775000e-05</td>\n",
       "      <td>170.538750</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>-0.002160</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-81.0</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>0.969</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>2.0</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-81.0</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>2</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.899140</td>\n",
       "      <td>1.494000e-05</td>\n",
       "      <td>-1.494000e-05</td>\n",
       "      <td>175.850252</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>-0.000581</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5853.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>-176.0</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>3</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1.0</td>\n",
       "      <td>5805.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>-174.0</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>4</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.525592</td>\n",
       "      <td>3.761000e-06</td>\n",
       "      <td>-3.761000e-06</td>\n",
       "      <td>171.595550</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>-0.001130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6031.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>-211.0</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   koi_score  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_period  \\\n",
       "0      1.000              0              0              0    9.488036   \n",
       "1      0.969              0              0              0   54.418383   \n",
       "2      0.000              0              1              0   19.899140   \n",
       "3      0.000              0              1              0    1.736952   \n",
       "4      1.000              0              0              0    2.525592   \n",
       "\n",
       "   koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "0     2.775000e-05    -2.775000e-05   170.538750          0.002160   \n",
       "1     2.479000e-04    -2.479000e-04   162.513840          0.003520   \n",
       "2     1.494000e-05    -1.494000e-05   175.850252          0.000581   \n",
       "3     2.630000e-07    -2.630000e-07   170.307565          0.000115   \n",
       "4     3.761000e-06    -3.761000e-06   171.595550          0.001130   \n",
       "\n",
       "   koi_time0bk_err2  ...  koi_tce_plnt_num  koi_steff  koi_steff_err1  \\\n",
       "0         -0.002160  ...               1.0     5455.0            81.0   \n",
       "1         -0.003520  ...               2.0     5455.0            81.0   \n",
       "2         -0.000581  ...               1.0     5853.0           158.0   \n",
       "3         -0.000115  ...               1.0     5805.0           157.0   \n",
       "4         -0.001130  ...               1.0     6031.0           169.0   \n",
       "\n",
       "   koi_steff_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "0           -81.0     0.927          0.105         -0.061  291.93423   \n",
       "1           -81.0     0.927          0.105         -0.061  291.93423   \n",
       "2          -176.0     0.868          0.233         -0.078  297.00482   \n",
       "3          -174.0     0.791          0.201         -0.067  285.53461   \n",
       "4          -211.0     1.046          0.334         -0.133  288.75488   \n",
       "\n",
       "         dec  koi_kepmag  \n",
       "0  48.141651      15.347  \n",
       "1  48.141651      15.347  \n",
       "2  48.134129      15.436  \n",
       "3  48.285210      15.597  \n",
       "4  48.226200      15.509  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "6cdda394-4db8-4108-8786-f00c52041024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['koi_score', 'koi_fpflag_nt', 'koi_fpflag_ss', 'koi_fpflag_co',\n",
      "       'koi_period', 'koi_period_err1', 'koi_period_err2', 'koi_time0bk',\n",
      "       'koi_time0bk_err1', 'koi_time0bk_err2', 'koi_impact', 'koi_impact_err1',\n",
      "       'koi_impact_err2', 'koi_duration', 'koi_duration_err1',\n",
      "       'koi_duration_err2', 'koi_depth', 'koi_depth_err1', 'koi_depth_err2',\n",
      "       'koi_prad', 'koi_prad_err1', 'koi_prad_err2', 'koi_model_snr',\n",
      "       'koi_tce_plnt_num', 'koi_steff', 'koi_steff_err1', 'koi_steff_err2',\n",
      "       'koi_srad', 'koi_srad_err1', 'koi_srad_err2', 'ra', 'dec',\n",
      "       'koi_kepmag'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data_features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9f8818c7-3e6a-4811-ba0f-b535b5eee618",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_target = data[['koi_pdisposition']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "eef56e41-16b3-44ba-8e6a-c7a341305ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "4fbefa40-687a-457f-bc03-a80f366cbd0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "a908ffbf-7a17-4b84-9547-3afd186f16d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_features[['koi_score', 'koi_fpflag_nt', 'koi_fpflag_ss', 'koi_fpflag_co',\n",
    "       'koi_period', 'koi_period_err1', 'koi_period_err2', 'koi_time0bk',\n",
    "       'koi_time0bk_err1', 'koi_time0bk_err2', 'koi_impact', 'koi_impact_err1',\n",
    "       'koi_impact_err2', 'koi_duration', 'koi_duration_err1',\n",
    "       'koi_duration_err2', 'koi_depth', 'koi_depth_err1', 'koi_depth_err2',\n",
    "       'koi_prad', 'koi_prad_err1', 'koi_prad_err2', 'koi_model_snr',\n",
    "       'koi_tce_plnt_num', 'koi_steff', 'koi_steff_err1', 'koi_steff_err2',\n",
    "       'koi_srad', 'koi_srad_err1', 'koi_srad_err2', 'ra', 'dec',\n",
    "       'koi_kepmag']] = scaler.fit_transform(data_features[['koi_score', 'koi_fpflag_nt', 'koi_fpflag_ss', 'koi_fpflag_co',\n",
    "       'koi_period', 'koi_period_err1', 'koi_period_err2', 'koi_time0bk',\n",
    "       'koi_time0bk_err1', 'koi_time0bk_err2', 'koi_impact', 'koi_impact_err1',\n",
    "       'koi_impact_err2', 'koi_duration', 'koi_duration_err1',\n",
    "       'koi_duration_err2', 'koi_depth', 'koi_depth_err1', 'koi_depth_err2',\n",
    "       'koi_prad', 'koi_prad_err1', 'koi_prad_err2', 'koi_model_snr',\n",
    "       'koi_tce_plnt_num', 'koi_steff', 'koi_steff_err1', 'koi_steff_err2',\n",
    "       'koi_srad', 'koi_srad_err1', 'koi_srad_err2', 'ra', 'dec',\n",
    "       'koi_kepmag']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d2193e13-9d58-41cd-b132-bc7e7a4a454a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   koi_score  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_period  \\\n",
      "0   1.065683      -0.360171      -0.598716       -0.52361   -0.320503   \n",
      "1   1.000701      -0.360171      -0.598716       -0.52361    0.197005   \n",
      "2  -1.030526      -0.360171       1.670241       -0.52361   -0.200588   \n",
      "3  -1.030526      -0.360171       1.670241       -0.52361   -0.409780   \n",
      "4   1.065683      -0.360171      -0.598716       -0.52361   -0.400697   \n",
      "\n",
      "   koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
      "0        -0.183453         0.183453     0.218721         -0.264257   \n",
      "1        -0.139571         0.139571     0.080093         -0.197742   \n",
      "2        -0.186006         0.186006     0.310475         -0.341483   \n",
      "3        -0.188931         0.188931     0.214727         -0.364274   \n",
      "4        -0.188234         0.188234     0.236977         -0.314633   \n",
      "\n",
      "   koi_time0bk_err2  ...  koi_tce_plnt_num  koi_steff  koi_steff_err1  \\\n",
      "0          0.264257  ...         -0.370317  -0.297055       -1.278141   \n",
      "1          0.197742  ...          1.171617  -0.297055       -1.278141   \n",
      "2          0.341483  ...         -0.370317   0.207675        0.329057   \n",
      "3          0.364274  ...         -0.370317   0.146803        0.308185   \n",
      "4          0.314633  ...         -0.370317   0.433409        0.558657   \n",
      "\n",
      "   koi_steff_err2  koi_srad  koi_srad_err1  koi_srad_err2        ra       dec  \\\n",
      "0        1.049663 -0.127996      -0.249311       0.164990 -0.029139  1.196093   \n",
      "1        1.049663 -0.127996      -0.249311       0.164990 -0.029139  1.196093   \n",
      "2       -0.227969 -0.138045      -0.098945       0.155952  1.030117  1.194003   \n",
      "3       -0.201071 -0.151159      -0.136537       0.161800 -1.366031  1.235974   \n",
      "4       -0.698675 -0.107727       0.019703       0.126710 -0.693310  1.219581   \n",
      "\n",
      "   koi_kepmag  \n",
      "0    0.776930  \n",
      "1    0.776930  \n",
      "2    0.843600  \n",
      "3    0.964206  \n",
      "4    0.898285  \n",
      "\n",
      "[5 rows x 33 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data_features.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "59c05051-d1c9-443d-90c4-2745e5aaf18e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "cd1cf19a-deaf-43b8-8545-7c2a47d75cd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test= train_test_split(data_features, data_target, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "56ea24d8-d84a-4606-8873-86219ca4f0bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size (6242, 33)\n",
      "Testing Set size (1561, 33)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set size\", X_train.shape)\n",
    "print(\"Testing Set size\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "838f5229-4b87-45aa-ab44-535200edb298",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "3473faec-5cd2-48df-8b46-b9c8b839b4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "edc4a0b1-9677-4957-8423-000e32733f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-23 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-23 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-23 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-23 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-23 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-23 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-23 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-23 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-23 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-23 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-23 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-23 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-23 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-23 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-23 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" checked><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train.values.ravel()) #Train the model on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "33dc9807-cd57-400f-9e7d-aa3cadb3e1a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9982\n"
     ]
    }
   ],
   "source": [
    "train_accuracy = model.score(X_train, y_train)\n",
    "print(f\"Train Accuracy: {train_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "dd77c2c3-4801-4cc7-bd5f-5302044a403b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.9981\n"
     ]
    }
   ],
   "source": [
    "test_accuracy = model.score(X_test, y_test)\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\") #no-overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "15b27d6f-8da6-44c0-8e75-5a759ec14462",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "6c08aa08-5488-4316-b73e-46a43a989571",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "907793fc-1add-45ed-a70a-75bf6e387f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       801\n",
      "           1       1.00      1.00      1.00       760\n",
      "\n",
      "    accuracy                           1.00      1561\n",
      "   macro avg       1.00      1.00      1.00      1561\n",
      "weighted avg       1.00      1.00      1.00      1561\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "5945c048-3646-45f0-87b4-abd1e43bf477",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "51018c0b-05ca-4ee1-8840-8e2cad2095dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_flat = y_train.to_numpy().ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "bcf9a157-2fda-4eb0-985d-86d5a4aaa6b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3195, 3047])"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_train_flat) #not-imbalanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "98f52f8a-0851-4dc1-b6d0-53a9244351e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "056193cc-d6ae-4263-9d54-89c620e61837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [0 0 1 1 0 1 0 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predictions:\", y_pred[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "a976986e-8053-4fed-b059-8a4d5e871923",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Values: [[0]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [0]\n",
      " [1]\n",
      " [1]\n",
      " [0]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual Values:\", y_test[:10].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "3eb6bb35-deb6-4150-8462-b5e735ba5044",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "ae213723-7684-4f50-bf79-02324bd70ccf",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = { 'C': [0.01, 0.1, 1, 10, 100], 'penalty':['l1', 'l2'], 'solver': ['liblinear']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "961c155d-d383-48e0-b5ef-57b6d86565fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "bfa980b6-4d36-4495-9695-20fb16cc0d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(log_reg, param_grid, cv = 5, scoring = 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "baf22e5b-be4a-4175-a97c-3b1b5861c0a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-21 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-21 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-21 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-21 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-21 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-21 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-21 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-21 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-21 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-21 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-21 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-21 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-21 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-21 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-21 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-21 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100], &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1, 10, 100], &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: LogisticRegression</label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=100, solver=&#x27;liblinear&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=100, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10, 100], 'penalty': ['l1', 'l2'],\n",
       "                         'solver': ['liblinear']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train.to_numpy().ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "698a1320-43d6-47df-85eb-a691d6b96fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_accuracy = grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "0ae71ca8-3f6a-4688-b7c6-aa65638ebaf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "Best Accuracy from cross-validation: 0.9971\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters:\", best_params)\n",
    "print(f\"Best Accuracy from cross-validation: {best_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "c1c50f4c-0e1d-4c44-9d63-bada6482904f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy after tuning: 0.9981\n"
     ]
    }
   ],
   "source": [
    "best_model = grid_search.best_estimator_\n",
    "test_accuracy = best_model.score(X_test, y_test)\n",
    "print(f\"Test Accuracy after tuning: {test_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "18293a0d-b049-4b36-b8b3-a12cc6590bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "a3ec5dfa-6bbe-4005-80c3-2183cac6a1d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.plot(data_features['koi_period'], data_features['koi_depth'], label = 'Star Brightness')\n",
    "plt.title('Time vs Flux')\n",
    "plt.xlabel('Time (days)')\n",
    "plt.ylabel('Flux (brightness)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "c12f3e34-a624-4d14-9ffc-88a1e100a359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.countplot(data_target['koi_pdisposition'])\n",
    "plt.title('Exoplanet Detection Distribution')\n",
    "plt.xlabel('Exoplanet Detected (1: Yes, 0: No)')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}