EPLogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sklearn\n",
      "  Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn in c:\\users\\gloam\\anaconda3\\lib\\site-packages (from sklearn) (0.22.1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.17.0 in c:\\users\\gloam\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.4.1)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\gloam\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.14.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.11.0 in c:\\users\\gloam\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.18.1)\n",
      "Building wheels for collected packages: sklearn\n",
      "  Building wheel for sklearn (setup.py): started\n",
      "  Building wheel for sklearn (setup.py): finished with status 'done'\n",
      "  Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1320 sha256=c773649d0350068599c9ec9ccf656b071efe5b58c413c1b4021bca1d9a2a98cf\n",
      "  Stored in directory: c:\\users\\gloam\\appdata\\local\\pip\\cache\\wheels\\46\\ef\\c3\\157e41f5ee1372d1be90b09f74f82b10e391eaacca8f22d33e\n",
      "Successfully built sklearn\n",
      "Installing collected packages: sklearn\n",
      "Successfully installed sklearn-0.0\n"
     ]
    }
   ],
   "source": [
    "# Update sklearn to prevent version mismatches\n",
    "\n",
    "#!pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install joblib. This will be used to save your model. \n",
    "# Restart your kernel after installing \n",
    "\n",
    "#!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Dependencies\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFECV\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>koi_disposition</th>\n",
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       "      <td>-2.630000e-07</td>\n",
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       "      <td>0.000115</td>\n",
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       "      <td>-174</td>\n",
       "      <td>4.564</td>\n",
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       "      <td>0.231</td>\n",
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       "      <td>0.313</td>\n",
       "      <td>-0.228</td>\n",
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       "      <th>6989</th>\n",
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       "      <td>-0.216</td>\n",
       "      <td>1.193</td>\n",
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       "      <td>47.121021</td>\n",
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       "<p>6991 rows × 41 columns</p>\n",
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      ],
      "text/plain": [
       "     koi_disposition  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  \\\n",
       "0          CONFIRMED              0              0              0   \n",
       "1     FALSE POSITIVE              0              1              0   \n",
       "2     FALSE POSITIVE              0              1              0   \n",
       "3          CONFIRMED              0              0              0   \n",
       "4          CONFIRMED              0              0              0   \n",
       "...              ...            ...            ...            ...   \n",
       "6986  FALSE POSITIVE              0              0              0   \n",
       "6987  FALSE POSITIVE              0              1              1   \n",
       "6988       CANDIDATE              0              0              0   \n",
       "6989  FALSE POSITIVE              0              0              1   \n",
       "6990  FALSE POSITIVE              0              0              1   \n",
       "\n",
       "      koi_fpflag_ec  koi_period  koi_period_err1  koi_period_err2  \\\n",
       "0                 0   54.418383     2.479000e-04    -2.479000e-04   \n",
       "1                 0   19.899140     1.490000e-05    -1.490000e-05   \n",
       "2                 0    1.736952     2.630000e-07    -2.630000e-07   \n",
       "3                 0    2.525592     3.760000e-06    -3.760000e-06   \n",
       "4                 0    4.134435     1.050000e-05    -1.050000e-05   \n",
       "...             ...         ...              ...              ...   \n",
       "6986              1    8.589871     1.846000e-04    -1.846000e-04   \n",
       "6987              0    0.527699     1.160000e-07    -1.160000e-07   \n",
       "6988              0    1.739849     1.780000e-05    -1.780000e-05   \n",
       "6989              0    0.681402     2.430000e-06    -2.430000e-06   \n",
       "6990              1    4.856035     6.360000e-05    -6.360000e-05   \n",
       "\n",
       "      koi_time0bk  koi_time0bk_err1  ...  koi_steff_err2  koi_slogg  \\\n",
       "0      162.513840          0.003520  ...             -81      4.467   \n",
       "1      175.850252          0.000581  ...            -176      4.544   \n",
       "2      170.307565          0.000115  ...            -174      4.564   \n",
       "3      171.595550          0.001130  ...            -211      4.438   \n",
       "4      172.979370          0.001900  ...            -232      4.486   \n",
       "...           ...               ...  ...             ...        ...   \n",
       "6986   132.016100          0.015700  ...            -152      4.296   \n",
       "6987   131.705093          0.000170  ...            -166      4.529   \n",
       "6988   133.001270          0.007690  ...            -220      4.444   \n",
       "6989   132.181750          0.002850  ...            -236      4.447   \n",
       "6990   135.993300          0.010800  ...            -225      4.385   \n",
       "\n",
       "      koi_slogg_err1  koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2  \\\n",
       "0              0.064          -0.096     0.927          0.105         -0.061   \n",
       "1              0.044          -0.176     0.868          0.233         -0.078   \n",
       "2              0.053          -0.168     0.791          0.201         -0.067   \n",
       "3              0.070          -0.210     1.046          0.334         -0.133   \n",
       "4              0.054          -0.229     0.972          0.315         -0.105   \n",
       "...              ...             ...       ...            ...            ...   \n",
       "6986           0.231          -0.189     1.088          0.313         -0.228   \n",
       "6987           0.035          -0.196     0.903          0.237         -0.079   \n",
       "6988           0.056          -0.224     1.031          0.341         -0.114   \n",
       "6989           0.056          -0.224     1.041          0.341         -0.114   \n",
       "6990           0.054          -0.216     1.193          0.410         -0.137   \n",
       "\n",
       "             ra        dec  koi_kepmag  \n",
       "0     291.93423  48.141651      15.347  \n",
       "1     297.00482  48.134129      15.436  \n",
       "2     285.53461  48.285210      15.597  \n",
       "3     288.75488  48.226200      15.509  \n",
       "4     296.28613  48.224670      15.714  \n",
       "...         ...        ...         ...  \n",
       "6986  298.74921  46.973351      14.478  \n",
       "6987  297.18875  47.093819      14.082  \n",
       "6988  286.50937  47.163219      14.757  \n",
       "6989  294.16489  47.176281      15.385  \n",
       "6990  297.00977  47.121021      14.826  \n",
       "\n",
       "[6991 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"exoplanet_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>koi_time0bk</th>\n",
       "      <th>koi_time0bk_err1</th>\n",
       "      <th>...</th>\n",
       "      <th>koi_steff_err2</th>\n",
       "      <th>koi_slogg</th>\n",
       "      <th>koi_slogg_err1</th>\n",
       "      <th>koi_slogg_err2</th>\n",
       "      <th>koi_srad</th>\n",
       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>koi_kepmag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.418383</td>\n",
       "      <td>2.479000e-04</td>\n",
       "      <td>-2.479000e-04</td>\n",
       "      <td>162.513840</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>...</td>\n",
       "      <td>-81</td>\n",
       "      <td>4.467</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.105</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>291.93423</td>\n",
       "      <td>48.141651</td>\n",
       "      <td>15.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.899140</td>\n",
       "      <td>1.490000e-05</td>\n",
       "      <td>-1.490000e-05</td>\n",
       "      <td>175.850252</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>...</td>\n",
       "      <td>-176</td>\n",
       "      <td>4.544</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.233</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>297.00482</td>\n",
       "      <td>48.134129</td>\n",
       "      <td>15.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.736952</td>\n",
       "      <td>2.630000e-07</td>\n",
       "      <td>-2.630000e-07</td>\n",
       "      <td>170.307565</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>...</td>\n",
       "      <td>-174</td>\n",
       "      <td>4.564</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.168</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>285.53461</td>\n",
       "      <td>48.285210</td>\n",
       "      <td>15.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.525592</td>\n",
       "      <td>3.760000e-06</td>\n",
       "      <td>-3.760000e-06</td>\n",
       "      <td>171.595550</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>...</td>\n",
       "      <td>-211</td>\n",
       "      <td>4.438</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>1.046</td>\n",
       "      <td>0.334</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>288.75488</td>\n",
       "      <td>48.226200</td>\n",
       "      <td>15.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.134435</td>\n",
       "      <td>1.050000e-05</td>\n",
       "      <td>-1.050000e-05</td>\n",
       "      <td>172.979370</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>...</td>\n",
       "      <td>-232</td>\n",
       "      <td>4.486</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.315</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>296.28613</td>\n",
       "      <td>48.224670</td>\n",
       "      <td>15.714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  koi_disposition  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  \\\n",
       "0       CONFIRMED              0              0              0              0   \n",
       "1  FALSE POSITIVE              0              1              0              0   \n",
       "2  FALSE POSITIVE              0              1              0              0   \n",
       "3       CONFIRMED              0              0              0              0   \n",
       "4       CONFIRMED              0              0              0              0   \n",
       "\n",
       "   koi_period  koi_period_err1  koi_period_err2  koi_time0bk  \\\n",
       "0   54.418383     2.479000e-04    -2.479000e-04   162.513840   \n",
       "1   19.899140     1.490000e-05    -1.490000e-05   175.850252   \n",
       "2    1.736952     2.630000e-07    -2.630000e-07   170.307565   \n",
       "3    2.525592     3.760000e-06    -3.760000e-06   171.595550   \n",
       "4    4.134435     1.050000e-05    -1.050000e-05   172.979370   \n",
       "\n",
       "   koi_time0bk_err1  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "0          0.003520  ...             -81      4.467           0.064   \n",
       "1          0.000581  ...            -176      4.544           0.044   \n",
       "2          0.000115  ...            -174      4.564           0.053   \n",
       "3          0.001130  ...            -211      4.438           0.070   \n",
       "4          0.001900  ...            -232      4.486           0.054   \n",
       "\n",
       "   koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "0          -0.096     0.927          0.105         -0.061  291.93423   \n",
       "1          -0.176     0.868          0.233         -0.078  297.00482   \n",
       "2          -0.168     0.791          0.201         -0.067  285.53461   \n",
       "3          -0.210     1.046          0.334         -0.133  288.75488   \n",
       "4          -0.229     0.972          0.315         -0.105  296.28613   \n",
       "\n",
       "         dec  koi_kepmag  \n",
       "0  48.141651      15.347  \n",
       "1  48.134129      15.436  \n",
       "2  48.285210      15.597  \n",
       "3  48.226200      15.509  \n",
       "4  48.224670      15.714  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the null columns where all values are null\n",
    "data = df.dropna(axis='columns', how='all')\n",
    "# Drop the null rows\n",
    "data = df.dropna()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert dtypes of int64 to float64\n",
    "for column, content in data.items():\n",
    "    if data[column].dtype == 'int64':\n",
    "        data = data.astype({column: 'float64'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set features. This will also be used as your x values. **NO COLUMNS WERE DELETED, WAS NOT SURE WHAT WAS GOING TO BE USED\n",
    "\n",
    "#selected_features = df[['names', 'of', 'selected', 'features', 'here']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `koi_disposition` for the y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign data to X and y\n",
    "X = data.drop(\"koi_disposition\", axis=1)\n",
    "y = data[\"koi_disposition\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and testing groups\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>koi_fpflag_nt</th>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
       "      <th>koi_period</th>\n",
       "      <th>koi_period_err1</th>\n",
       "      <th>koi_period_err2</th>\n",
       "      <th>koi_time0bk</th>\n",
       "      <th>koi_time0bk_err1</th>\n",
       "      <th>koi_time0bk_err2</th>\n",
       "      <th>...</th>\n",
       "      <th>koi_steff_err2</th>\n",
       "      <th>koi_slogg</th>\n",
       "      <th>koi_slogg_err1</th>\n",
       "      <th>koi_slogg_err2</th>\n",
       "      <th>koi_srad</th>\n",
       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>koi_kepmag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6080</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.496435</td>\n",
       "      <td>2.213000e-04</td>\n",
       "      <td>-2.213000e-04</td>\n",
       "      <td>132.035800</td>\n",
       "      <td>0.014300</td>\n",
       "      <td>-0.014300</td>\n",
       "      <td>...</td>\n",
       "      <td>-286.0</td>\n",
       "      <td>3.805</td>\n",
       "      <td>0.390</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>2.730</td>\n",
       "      <td>0.535</td>\n",
       "      <td>-1.248</td>\n",
       "      <td>289.23080</td>\n",
       "      <td>44.412483</td>\n",
       "      <td>13.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3001</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.615625</td>\n",
       "      <td>1.528000e-04</td>\n",
       "      <td>-1.528000e-04</td>\n",
       "      <td>131.968430</td>\n",
       "      <td>0.008230</td>\n",
       "      <td>-0.008230</td>\n",
       "      <td>...</td>\n",
       "      <td>-72.0</td>\n",
       "      <td>4.083</td>\n",
       "      <td>0.368</td>\n",
       "      <td>-0.092</td>\n",
       "      <td>1.453</td>\n",
       "      <td>0.218</td>\n",
       "      <td>-0.510</td>\n",
       "      <td>293.52756</td>\n",
       "      <td>41.111439</td>\n",
       "      <td>15.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.980246</td>\n",
       "      <td>6.930000e-07</td>\n",
       "      <td>-6.930000e-07</td>\n",
       "      <td>137.137607</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>-0.000053</td>\n",
       "      <td>...</td>\n",
       "      <td>-159.0</td>\n",
       "      <td>4.462</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.182</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.238</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>282.79764</td>\n",
       "      <td>43.578129</td>\n",
       "      <td>14.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4897</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>466.908240</td>\n",
       "      <td>1.194000e-02</td>\n",
       "      <td>-1.194000e-02</td>\n",
       "      <td>136.373100</td>\n",
       "      <td>0.019000</td>\n",
       "      <td>-0.019000</td>\n",
       "      <td>...</td>\n",
       "      <td>-146.0</td>\n",
       "      <td>4.456</td>\n",
       "      <td>0.102</td>\n",
       "      <td>-0.361</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.103</td>\n",
       "      <td>297.65436</td>\n",
       "      <td>43.178551</td>\n",
       "      <td>15.202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.061933</td>\n",
       "      <td>1.250000e-06</td>\n",
       "      <td>-1.250000e-06</td>\n",
       "      <td>133.850441</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>-0.000978</td>\n",
       "      <td>...</td>\n",
       "      <td>-167.0</td>\n",
       "      <td>3.975</td>\n",
       "      <td>0.259</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>1.851</td>\n",
       "      <td>0.383</td>\n",
       "      <td>-0.575</td>\n",
       "      <td>288.90253</td>\n",
       "      <td>44.632992</td>\n",
       "      <td>12.953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  koi_period  \\\n",
       "6080            1.0            0.0            0.0            0.0   12.496435   \n",
       "3001            0.0            0.0            0.0            0.0   11.615625   \n",
       "570             0.0            1.0            0.0            0.0   10.980246   \n",
       "4897            1.0            0.0            0.0            0.0  466.908240   \n",
       "625             0.0            1.0            1.0            1.0    1.061933   \n",
       "\n",
       "      koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "6080     2.213000e-04    -2.213000e-04   132.035800          0.014300   \n",
       "3001     1.528000e-04    -1.528000e-04   131.968430          0.008230   \n",
       "570      6.930000e-07    -6.930000e-07   137.137607          0.000053   \n",
       "4897     1.194000e-02    -1.194000e-02   136.373100          0.019000   \n",
       "625      1.250000e-06    -1.250000e-06   133.850441          0.000978   \n",
       "\n",
       "      koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "6080         -0.014300  ...          -286.0      3.805           0.390   \n",
       "3001         -0.008230  ...           -72.0      4.083           0.368   \n",
       "570          -0.000053  ...          -159.0      4.462           0.098   \n",
       "4897         -0.019000  ...          -146.0      4.456           0.102   \n",
       "625          -0.000978  ...          -167.0      3.975           0.259   \n",
       "\n",
       "      koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "6080          -0.130     2.730          0.535         -1.248  289.23080   \n",
       "3001          -0.092     1.453          0.218         -0.510  293.52756   \n",
       "570           -0.182     0.897          0.238         -0.119  282.79764   \n",
       "4897          -0.361     0.867          0.448         -0.103  297.65436   \n",
       "625           -0.111     1.851          0.383         -0.575  288.90253   \n",
       "\n",
       "            dec  koi_kepmag  \n",
       "6080  44.412483      13.054  \n",
       "3001  41.111439      15.162  \n",
       "570   43.578129      14.212  \n",
       "4897  43.178551      15.202  \n",
       "625   44.632992      12.953  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5243, 40)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5243,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "\n",
    "Scale the data using the MinMaxScaler and perform some feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale X values\n",
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 85.504 %\n",
      "Testing Data Score: 86.213 %\n"
     ]
    }
   ],
   "source": [
    "#Train the model \n",
    "model_1 = LogisticRegression(solver='newton-cg', multi_class='auto')\n",
    "model_1.fit(X_train_scaled, y_train)\n",
    "\n",
    "model_1_training_score = round(model_1.score(X_train_scaled, y_train)*100,3)\n",
    "base_accuracy = round(model_1.score(X_test_scaled, y_test)*100,3)\n",
    "\n",
    "print(f\"Training Data Score: {model_1_training_score} %\")\n",
    "print(f\"Testing Data Score: {base_accuracy} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select Features. Evaluate features\n",
    "feature_names = X.columns.tolist()\n",
    "selector = RFECV(estimator=model_1, cv=5, step=1)\n",
    "_ = selector.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ranking</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_depth</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_duration</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_duration_err1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>koi_duration_err2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Ranking\n",
       "Feature                   \n",
       "dec                      1\n",
       "koi_depth                1\n",
       "koi_duration             1\n",
       "koi_duration_err1        1\n",
       "koi_duration_err2        1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which features ought to be kept\n",
    "preSelected_features = sorted(zip(selector.ranking_, feature_names))\n",
    "ranked_features = pd.DataFrame(preSelected_features, columns=['Ranking', 'Feature'])\n",
    "ranked_features = ranked_features.set_index('Feature')\n",
    "ranked_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'dec'),\n",
       " (1, 'koi_depth'),\n",
       " (1, 'koi_duration'),\n",
       " (1, 'koi_duration_err1'),\n",
       " (1, 'koi_duration_err2'),\n",
       " (1, 'koi_fpflag_co'),\n",
       " (1, 'koi_fpflag_ec'),\n",
       " (1, 'koi_fpflag_nt'),\n",
       " (1, 'koi_fpflag_ss'),\n",
       " (1, 'koi_impact'),\n",
       " (1, 'koi_impact_err1'),\n",
       " (1, 'koi_impact_err2'),\n",
       " (1, 'koi_model_snr'),\n",
       " (1, 'koi_period'),\n",
       " (1, 'koi_period_err1'),\n",
       " (1, 'koi_period_err2'),\n",
       " (1, 'koi_slogg'),\n",
       " (1, 'koi_slogg_err1'),\n",
       " (1, 'koi_slogg_err2'),\n",
       " (1, 'koi_srad'),\n",
       " (1, 'koi_srad_err1'),\n",
       " (1, 'koi_srad_err2'),\n",
       " (1, 'koi_steff'),\n",
       " (1, 'koi_steff_err1'),\n",
       " (1, 'koi_steff_err2'),\n",
       " (1, 'koi_tce_plnt_num'),\n",
       " (1, 'koi_teq'),\n",
       " (1, 'koi_time0bk'),\n",
       " (1, 'koi_time0bk_err1'),\n",
       " (1, 'koi_time0bk_err2'),\n",
       " (1, 'ra'),\n",
       " (2, 'koi_kepmag'),\n",
       " (3, 'koi_insol_err1'),\n",
       " (4, 'koi_prad_err1'),\n",
       " (5, 'koi_prad'),\n",
       " (6, 'koi_insol'),\n",
       " (7, 'koi_insol_err2'),\n",
       " (8, 'koi_prad_err2'),\n",
       " (9, 'koi_depth_err2'),\n",
       " (10, 'koi_depth_err1')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preSelected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove features with Ranking > 16\n",
    "selected_features = []\n",
    "for tup in preSelected_features:\n",
    "    if tup[0] < 17:\n",
    "        selected_features.append(tup[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 85.504 %\n",
      "Testing Data Score: 86.213 %\n"
     ]
    }
   ],
   "source": [
    "# Use new data for all subsequent models\n",
    "## Assign new data to X \n",
    "X_train_select = X_train[selected_features]\n",
    "X_test_select = X_test[selected_features]\n",
    "\n",
    "X_scaler = MinMaxScaler().fit(X_train_select)\n",
    "X_train_scaled = X_scaler.transform(X_train_select)\n",
    "X_test_scaled = X_scaler.transform(X_test_select)\n",
    "\n",
    "## Train new model\n",
    "model_2 = LogisticRegression(solver='newton-cg', multi_class='auto')\n",
    "model_2.fit(X_train_scaled, y_train)\n",
    "\n",
    "model_2_training_score = round(model_2.score(X_train_scaled, y_train)*100,3)\n",
    "select_features_accuracy = round(model_2.score(X_test_scaled, y_test)*100,3)\n",
    "\n",
    "print(f\"Training Data Score: {model_2_training_score} %\")\n",
    "print(f\"Testing Data Score: {select_features_accuracy} %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning\n",
    "\n",
    "Use `GridSearchCV` to tune the model's parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the GridSearchCV model\n",
    "model_3 = LogisticRegression(solver='newton-cg', multi_class='auto')\n",
    "\n",
    "param_grid = {\n",
    "    'C': np.logspace(0, 4, 10),\n",
    "    'penalty': ['l2']\n",
    "}\n",
    "grid = GridSearchCV(model_3, param_grid, cv=5, verbose=0)\n",
    "\n",
    "# Train the model with GridSearch\n",
    "_ = grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 88.709 %\n",
      "Testing Data Score: 89.474 %\n"
     ]
    }
   ],
   "source": [
    "# Train the model with GridSearch\n",
    "# Tuned parameters\n",
    "C = grid.best_params_['C']\n",
    "penalty = grid.best_params_['penalty']\n",
    "\n",
    "# Tuned model\n",
    "tuned_model = LogisticRegression(solver='newton-cg', multi_class='auto',\n",
    "                                 C=C, penalty=penalty)\n",
    "tuned_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "model_3_training_score = round(tuned_model.score(X_train_scaled, y_train)*100,3)\n",
    "tuned_accuracy = round(tuned_model.score(X_test_scaled, y_test)*100,3)\n",
    "\n",
    "print(f\"Training Data Score: {model_3_training_score} %\")\n",
    "print(f\"Testing Data Score: {tuned_accuracy} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8842261623210763\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL PREDICTIONS AND EVALUATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>CONFIRMED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual      Prediction\n",
       "0        CANDIDATE       CANDIDATE\n",
       "1   FALSE POSITIVE  FALSE POSITIVE\n",
       "2   FALSE POSITIVE  FALSE POSITIVE\n",
       "3   FALSE POSITIVE  FALSE POSITIVE\n",
       "4        CANDIDATE       CANDIDATE\n",
       "5   FALSE POSITIVE  FALSE POSITIVE\n",
       "6        CANDIDATE       CANDIDATE\n",
       "7   FALSE POSITIVE  FALSE POSITIVE\n",
       "8   FALSE POSITIVE  FALSE POSITIVE\n",
       "9   FALSE POSITIVE  FALSE POSITIVE\n",
       "10  FALSE POSITIVE  FALSE POSITIVE\n",
       "11  FALSE POSITIVE  FALSE POSITIVE\n",
       "12       CONFIRMED       CONFIRMED\n",
       "13  FALSE POSITIVE  FALSE POSITIVE\n",
       "14  FALSE POSITIVE  FALSE POSITIVE"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PREDICTIONS\n",
    "predictions = tuned_model.predict(X_test_scaled)\n",
    "classifications = y_test.unique().tolist()\n",
    "\n",
    "prediction_actual = {\n",
    "    'Actual': y_test,\n",
    "    'Prediction': predictions\n",
    "}\n",
    "\n",
    "PA_df = pd.DataFrame(prediction_actual)\n",
    "PA_df = PA_df.set_index('Actual').reset_index()\n",
    "PA_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Base Model</th>\n",
       "      <td>86.213%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Select Features Model</th>\n",
       "      <td>86.213%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuned Model</th>\n",
       "      <td>89.474%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Accuracy\n",
       "                              \n",
       "Base Model             86.213%\n",
       "Select Features Model  86.213%\n",
       "Tuned Model            89.474%"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#EVALUATIONS\n",
    "evaluations = {'': ['Base Model', 'Select Features Model', 'Tuned Model'],\n",
    "               'Accuracy': [f\"{base_accuracy}%\", f\"{select_features_accuracy}%\", f\"{tuned_accuracy}%\"]}\n",
    "\n",
    "evaluations_df = pd.DataFrame(evaluations)\n",
    "evaluations_df = evaluations_df.set_index('')\n",
    "\n",
    "evaluations_df.to_csv('output/LogisticRegression_evaluate.csv')\n",
    "evaluations_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/EPLogisticRegression_evaluate.sav']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save your model by updating \"your_name\" with your name\n",
    "# and \"your_model\" with your model variable\n",
    "# be sure to turn this in to BCS\n",
    "# if joblib fails to import, try running the command to install in terminal/git-bash\n",
    "\n",
    "import joblib\n",
    "filename = ('models/EPLogisticRegression_evaluate.sav')\n",
    "joblib.dump(grid,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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