From 900206b2fe082ef6ae8cbc9274d7c8da19ad7713 Mon Sep 17 00:00:00 2001
From: Mert Ozcan <93086504+Pierciest@users.noreply.github.com>
Date: Sun, 6 Oct 2024 02:56:17 +0200
Subject: [PATCH] Delete NASA exoplanet classification models directory

---
 .../exoplanet_classification.ipynb            | 1422 ----
 .../exoplanet_data.csv                        | 6992 -----------------
 2 files changed, 8414 deletions(-)
 delete mode 100644 NASA exoplanet classification models/exoplanet_classification.ipynb
 delete mode 100644 NASA exoplanet classification models/exoplanet_data.csv

diff --git a/NASA exoplanet classification models/exoplanet_classification.ipynb b/NASA exoplanet classification models/exoplanet_classification.ipynb
deleted file mode 100644
index 7f6721e..0000000
--- a/NASA exoplanet classification models/exoplanet_classification.ipynb	
+++ /dev/null
@@ -1,1422 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exploratiray Data analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Data Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(\"exoplanet_data.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
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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",
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-       "      <th>...</th>\n",
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-       "      <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",
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-       "      <td>0</td>\n",
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-       "    </tr>\n",
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-       "      <th>4</th>\n",
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-       "      <td>47.121021</td>\n",
-       "      <td>14.826</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6991 rows × 41 columns</p>\n",
-       "</div>"
-      ],
-      "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
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-       "      <th>koi_fpflag_nt</th>\n",
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-       "      <th>koi_slogg</th>\n",
-       "      <th>koi_slogg_err1</th>\n",
-       "      <th>koi_slogg_err2</th>\n",
-       "      <th>koi_srad</th>\n",
-       "      <th>koi_srad_err1</th>\n",
-       "      <th>koi_srad_err2</th>\n",
-       "      <th>ra</th>\n",
-       "      <th>dec</th>\n",
-       "      <th>koi_kepmag</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>54.418383</td>\n",
-       "      <td>2.479000e-04</td>\n",
-       "      <td>-2.479000e-04</td>\n",
-       "      <td>162.513840</td>\n",
-       "      <td>0.003520</td>\n",
-       "      <td>-0.003520</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-81</td>\n",
-       "      <td>4.467</td>\n",
-       "      <td>0.064</td>\n",
-       "      <td>-0.096</td>\n",
-       "      <td>0.927</td>\n",
-       "      <td>0.105</td>\n",
-       "      <td>-0.061</td>\n",
-       "      <td>291.93423</td>\n",
-       "      <td>48.141651</td>\n",
-       "      <td>15.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>19.899140</td>\n",
-       "      <td>1.490000e-05</td>\n",
-       "      <td>-1.490000e-05</td>\n",
-       "      <td>175.850252</td>\n",
-       "      <td>0.000581</td>\n",
-       "      <td>-0.000581</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-176</td>\n",
-       "      <td>4.544</td>\n",
-       "      <td>0.044</td>\n",
-       "      <td>-0.176</td>\n",
-       "      <td>0.868</td>\n",
-       "      <td>0.233</td>\n",
-       "      <td>-0.078</td>\n",
-       "      <td>297.00482</td>\n",
-       "      <td>48.134129</td>\n",
-       "      <td>15.436</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.736952</td>\n",
-       "      <td>2.630000e-07</td>\n",
-       "      <td>-2.630000e-07</td>\n",
-       "      <td>170.307565</td>\n",
-       "      <td>0.000115</td>\n",
-       "      <td>-0.000115</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-174</td>\n",
-       "      <td>4.564</td>\n",
-       "      <td>0.053</td>\n",
-       "      <td>-0.168</td>\n",
-       "      <td>0.791</td>\n",
-       "      <td>0.201</td>\n",
-       "      <td>-0.067</td>\n",
-       "      <td>285.53461</td>\n",
-       "      <td>48.285210</td>\n",
-       "      <td>15.597</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.525592</td>\n",
-       "      <td>3.760000e-06</td>\n",
-       "      <td>-3.760000e-06</td>\n",
-       "      <td>171.595550</td>\n",
-       "      <td>0.001130</td>\n",
-       "      <td>-0.001130</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-211</td>\n",
-       "      <td>4.438</td>\n",
-       "      <td>0.070</td>\n",
-       "      <td>-0.210</td>\n",
-       "      <td>1.046</td>\n",
-       "      <td>0.334</td>\n",
-       "      <td>-0.133</td>\n",
-       "      <td>288.75488</td>\n",
-       "      <td>48.226200</td>\n",
-       "      <td>15.509</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.134435</td>\n",
-       "      <td>1.050000e-05</td>\n",
-       "      <td>-1.050000e-05</td>\n",
-       "      <td>172.979370</td>\n",
-       "      <td>0.001900</td>\n",
-       "      <td>-0.001900</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-232</td>\n",
-       "      <td>4.486</td>\n",
-       "      <td>0.054</td>\n",
-       "      <td>-0.229</td>\n",
-       "      <td>0.972</td>\n",
-       "      <td>0.315</td>\n",
-       "      <td>-0.105</td>\n",
-       "      <td>296.28613</td>\n",
-       "      <td>48.224670</td>\n",
-       "      <td>15.714</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 40 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  koi_period  \\\n",
-       "0              0              0              0              0   54.418383   \n",
-       "1              0              1              0              0   19.899140   \n",
-       "2              0              1              0              0    1.736952   \n",
-       "3              0              0              0              0    2.525592   \n",
-       "4              0              0              0              0    4.134435   \n",
-       "\n",
-       "   koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
-       "0     2.479000e-04    -2.479000e-04   162.513840          0.003520   \n",
-       "1     1.490000e-05    -1.490000e-05   175.850252          0.000581   \n",
-       "2     2.630000e-07    -2.630000e-07   170.307565          0.000115   \n",
-       "3     3.760000e-06    -3.760000e-06   171.595550          0.001130   \n",
-       "4     1.050000e-05    -1.050000e-05   172.979370          0.001900   \n",
-       "\n",
-       "   koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
-       "0         -0.003520  ...             -81      4.467           0.064   \n",
-       "1         -0.000581  ...            -176      4.544           0.044   \n",
-       "2         -0.000115  ...            -174      4.564           0.053   \n",
-       "3         -0.001130  ...            -211      4.438           0.070   \n",
-       "4         -0.001900  ...            -232      4.486           0.054   \n",
-       "\n",
-       "   koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
-       "0          -0.096     0.927          0.105         -0.061  291.93423   \n",
-       "1          -0.176     0.868          0.233         -0.078  297.00482   \n",
-       "2          -0.168     0.791          0.201         -0.067  285.53461   \n",
-       "3          -0.210     1.046          0.334         -0.133  288.75488   \n",
-       "4          -0.229     0.972          0.315         -0.105  296.28613   \n",
-       "\n",
-       "         dec  koi_kepmag  \n",
-       "0  48.141651      15.347  \n",
-       "1  48.134129      15.436  \n",
-       "2  48.285210      15.597  \n",
-       "3  48.226200      15.509  \n",
-       "4  48.224670      15.714  \n",
-       "\n",
-       "[5 rows x 40 columns]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Set features. This will also be used as x values. Here all of the columns except \"koi_disposition\" are selected as features. \n",
-    "selected_features = df.drop(\"koi_disposition\", axis=1)\n",
-    "selected_features.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = selected_features\n",
-    "y = df[\"koi_disposition\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, RobustScaler\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 1, stratify =y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "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>4002</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>99.673478</td>\n",
-       "      <td>3.463000e-04</td>\n",
-       "      <td>-3.463000e-04</td>\n",
-       "      <td>219.334830</td>\n",
-       "      <td>0.002300</td>\n",
-       "      <td>-0.002300</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-148</td>\n",
-       "      <td>4.777</td>\n",
-       "      <td>0.040</td>\n",
-       "      <td>-0.027</td>\n",
-       "      <td>0.492</td>\n",
-       "      <td>0.026</td>\n",
-       "      <td>-0.027</td>\n",
-       "      <td>293.05801</td>\n",
-       "      <td>45.248821</td>\n",
-       "      <td>15.801</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4246</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.592244</td>\n",
-       "      <td>9.000000e-08</td>\n",
-       "      <td>-9.000000e-08</td>\n",
-       "      <td>131.654831</td>\n",
-       "      <td>0.000124</td>\n",
-       "      <td>-0.000124</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-146</td>\n",
-       "      <td>4.664</td>\n",
-       "      <td>0.056</td>\n",
-       "      <td>-0.032</td>\n",
-       "      <td>0.591</td>\n",
-       "      <td>0.045</td>\n",
-       "      <td>-0.045</td>\n",
-       "      <td>290.28094</td>\n",
-       "      <td>45.464260</td>\n",
-       "      <td>15.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>548</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>9.991625</td>\n",
-       "      <td>5.360000e-06</td>\n",
-       "      <td>-5.360000e-06</td>\n",
-       "      <td>137.447816</td>\n",
-       "      <td>0.000445</td>\n",
-       "      <td>-0.000445</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-176</td>\n",
-       "      <td>4.338</td>\n",
-       "      <td>0.153</td>\n",
-       "      <td>-0.187</td>\n",
-       "      <td>1.096</td>\n",
-       "      <td>0.309</td>\n",
-       "      <td>-0.206</td>\n",
-       "      <td>301.04239</td>\n",
-       "      <td>45.022888</td>\n",
-       "      <td>14.039</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3953</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>178.412990</td>\n",
-       "      <td>3.100000e-05</td>\n",
-       "      <td>-3.100000e-05</td>\n",
-       "      <td>218.225235</td>\n",
-       "      <td>0.000127</td>\n",
-       "      <td>-0.000127</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-134</td>\n",
-       "      <td>4.346</td>\n",
-       "      <td>0.084</td>\n",
-       "      <td>-0.126</td>\n",
-       "      <td>1.148</td>\n",
-       "      <td>0.202</td>\n",
-       "      <td>-0.124</td>\n",
-       "      <td>288.32785</td>\n",
-       "      <td>38.627621</td>\n",
-       "      <td>13.944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2362</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>45.294223</td>\n",
-       "      <td>5.600000e-05</td>\n",
-       "      <td>-5.600000e-05</td>\n",
-       "      <td>138.678725</td>\n",
-       "      <td>0.000987</td>\n",
-       "      <td>-0.000987</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-68</td>\n",
-       "      <td>4.347</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>-0.030</td>\n",
-       "      <td>1.044</td>\n",
-       "      <td>0.057</td>\n",
-       "      <td>-0.042</td>\n",
-       "      <td>285.67938</td>\n",
-       "      <td>50.241299</td>\n",
-       "      <td>10.961</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",
-       "4002              0              0              1              0   99.673478   \n",
-       "4246              0              1              0              0    0.592244   \n",
-       "548               0              1              1              0    9.991625   \n",
-       "3953              0              1              0              0  178.412990   \n",
-       "2362              0              0              0              0   45.294223   \n",
-       "\n",
-       "      koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
-       "4002     3.463000e-04    -3.463000e-04   219.334830          0.002300   \n",
-       "4246     9.000000e-08    -9.000000e-08   131.654831          0.000124   \n",
-       "548      5.360000e-06    -5.360000e-06   137.447816          0.000445   \n",
-       "3953     3.100000e-05    -3.100000e-05   218.225235          0.000127   \n",
-       "2362     5.600000e-05    -5.600000e-05   138.678725          0.000987   \n",
-       "\n",
-       "      koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
-       "4002         -0.002300  ...            -148      4.777           0.040   \n",
-       "4246         -0.000124  ...            -146      4.664           0.056   \n",
-       "548          -0.000445  ...            -176      4.338           0.153   \n",
-       "3953         -0.000127  ...            -134      4.346           0.084   \n",
-       "2362         -0.000987  ...             -68      4.347           0.030   \n",
-       "\n",
-       "      koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
-       "4002          -0.027     0.492          0.026         -0.027  293.05801   \n",
-       "4246          -0.032     0.591          0.045         -0.045  290.28094   \n",
-       "548           -0.187     1.096          0.309         -0.206  301.04239   \n",
-       "3953          -0.126     1.148          0.202         -0.124  288.32785   \n",
-       "2362          -0.030     1.044          0.057         -0.042  285.67938   \n",
-       "\n",
-       "            dec  koi_kepmag  \n",
-       "4002  45.248821      15.801  \n",
-       "4246  45.464260      15.653  \n",
-       "548   45.022888      14.039  \n",
-       "3953  38.627621      13.944  \n",
-       "2362  50.241299      10.961  \n",
-       "\n",
-       "[5 rows x 40 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0         CONFIRMED\n",
-       "1    FALSE POSITIVE\n",
-       "2    FALSE POSITIVE\n",
-       "3         CONFIRMED\n",
-       "4         CONFIRMED\n",
-       "Name: koi_disposition, dtype: object"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5243, 40)\n",
-      "(5243,)\n",
-      "(1748, 40)\n",
-      "(1748,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(X_train.shape)\n",
-    "print(y_train.shape)\n",
-    "print(X_test.shape)\n",
-    "print(y_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Scale data using Robust scaler\n",
-    "X_train_scaled = RobustScaler().fit(X_train)\n",
-    "X_test_scaled = X_train_scaled.transform(X_test)\n",
-    "X_train_scaled = X_train_scaled.transform(X_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model Training"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Machine Learning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Support Vector Classification (SVC)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.svm import SVC\n",
-    "svc_model = SVC(kernel='linear')\n",
-    "svc_model.fit(X_train_scaled, y_train)\n",
-    "# predictions = svc_model.predict(X_test)\n",
-    "svm_predictions = svc_model.predict(X_test_scaled)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Data Score: 0.900057219149342\n",
-      "Testing Data Score: 0.8832951945080092\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Training Data Score: {svc_model.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score: {svc_model.score(X_test_scaled, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create the GridSearchCV model\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "paramgrid = {\n",
-    "                \"C\": [0.1,1,10,50,100],\n",
-    "                \"gamma\" : [0.0001, 0.001, 0.01, 0.1]\n",
-    "            }\n",
-    "svm_grid_model = GridSearchCV(svc_model, paramgrid)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Train the model with GridSearch\n",
-    "svm_grid_model.fit(X_train_scaled, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(svm_grid_model.best_params_)\n",
-    "print(svm_grid_model.best_score_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Training Data Score: {svm_grid_model.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score: {svm_grid_model.score(X_test_scaled, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "svm_grid_predictions = svm_grid_model.predict(X_test_scaled)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import classification_report\n",
-    "print(classification_report(y_test, svm_grid_predictions))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save model with model variable\n",
-    "import joblib\n",
-    "filename = 'SVC_model.sav'\n",
-    "joblib.dump(svm_grid_model, filename)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Random Forest"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "clf = RandomForestClassifier()\n",
-    "clf = clf.fit(X_train_scaled, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Training Data Score with Random Forest Classifier: {clf.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score with Random Forest Classifier: {clf.score(X_test_scaled, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "paramgrid1 = {\"n_estimators\": [50, 150, 250],\n",
-    "             \"max_depth\":[100, 200, 300] }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf_grid_model = GridSearchCV(clf, paramgrid1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf_grid_model.fit(X_train_scaled, y_train)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(clf_grid_model.best_score_)\n",
-    "print(clf_grid_model.best_params_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Training Data Score with Random Forest Classifier with grid search: {clf_grid_model.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score with Random Forest Classifier with grid search: {clf_grid_model.score(X_test_scaled, y_test)}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rf_clf_prediction = clf_grid_model.predict(X_test_scaled)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(classification_report(y_test, rf_clf_prediction))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save model with model variable\n",
-    "filename = 'RF_model.sav'\n",
-    "joblib.dump(clf_grid_model, filename)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "joblib.load('RF_model.sav')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Linear model (Logistic Regression, Linear Regression)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression, LinearRegression"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lcf = LogisticRegression()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lcf.fit(X_train_scaled, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Training Data Score with Logistic Regression Classifier: {lcf.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score with Logistic Regression Classifier: {lcf.score(X_test_scaled, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(lcf.predict(X_test_scaled))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### K-Neihbors Classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.neighbors import KNeighborsClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn = KNeighborsClassifier(n_neighbors=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn.fit(X_train_scaled, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Training Data Score with KNN Classifier: {knn.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score with KNN Classifier: {knn.score(X_test_scaled, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "paramgrid_knn = {\"n_neighbors\": [5, 10, 20] }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_knn = GridSearchCV(knn, paramgrid_knn)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_knn.fit(X_train_scaled, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_knn.best_params_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Training Data Score with KNN Classifier with Gridsearch: {knn.score(X_train_scaled, y_train)}\")\n",
-    "print(f\"Testing Data Score with KNN Classifier with Gridsearch: {knn.score(X_test_scaled, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save model with model variable\n",
-    "filename = 'KNN_model.sav'\n",
-    "joblib.dump(knn, filename)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### XGBoost"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Neural Networks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Model ensembling techniques"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Synthetic Data generation"
-   ]
-  }
- ],
- "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": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/NASA exoplanet classification models/exoplanet_data.csv b/NASA exoplanet classification models/exoplanet_data.csv
deleted file mode 100644
index 4fdbd7f..0000000
--- a/NASA exoplanet classification models/exoplanet_data.csv	
+++ /dev/null
@@ -1,6992 +0,0 @@
-koi_disposition,koi_fpflag_nt,koi_fpflag_ss,koi_fpflag_co,koi_fpflag_ec,koi_period,koi_period_err1,koi_period_err2,koi_time0bk,koi_time0bk_err1,koi_time0bk_err2,koi_impact,koi_impact_err1,koi_impact_err2,koi_duration,koi_duration_err1,koi_duration_err2,koi_depth,koi_depth_err1,koi_depth_err2,koi_prad,koi_prad_err1,koi_prad_err2,koi_teq,koi_insol,koi_insol_err1,koi_insol_err2,koi_model_snr,koi_tce_plnt_num,koi_steff,koi_steff_err1,koi_steff_err2,koi_slogg,koi_slogg_err1,koi_slogg_err2,koi_srad,koi_srad_err1,koi_srad_err2,ra,dec,koi_kepmag
-CONFIRMED,0,0,0,0,54.4183827,0.0002479,-0.0002479,162.51384,0.00352,-0.00352,0.586,0.059,-0.443,4.507,0.116,-0.116,874.8,35.5,-35.5,2.83,0.32,-0.19,443,9.11,2.87,-1.62,25.8,2,5455,81,-81,4.467,0.064,-0.096,0.927,0.105,-0.061,291.93423,48.141651,15.347
-FALSE POSITIVE,0,1,0,0,19.89913995,1.49E-05,-1.49E-05,175.850252,0.000581,-0.000581,0.969,5.126,-0.077,1.7822,0.0341,-0.0341,10829,171,-171,14.6,3.92,-1.31,638,39.3,31.04,-10.49,76.3,1,5853,158,-176,4.544,0.044,-0.176,0.868,0.233,-0.078,297.00482,48.134129,15.436
-FALSE POSITIVE,0,1,0,0,1.736952453,2.63E-07,-2.63E-07,170.307565,0.000115,-0.000115,1.276,0.115,-0.092,2.40641,0.00537,-0.00537,8079.2,12.8,-12.8,33.46,8.5,-2.83,1395,891.96,668.95,-230.35,505.6,1,5805,157,-174,4.564,0.053,-0.168,0.791,0.201,-0.067,285.53461,48.28521,15.597
-CONFIRMED,0,0,0,0,2.525591777,3.76E-06,-3.76E-06,171.59555,0.00113,-0.00113,0.701,0.235,-0.478,1.6545,0.042,-0.042,603.3,16.9,-16.9,2.75,0.88,-0.35,1406,926.16,874.33,-314.24,40.9,1,6031,169,-211,4.438,0.07,-0.21,1.046,0.334,-0.133,288.75488,48.2262,15.509
-CONFIRMED,0,0,0,0,4.13443512,1.05E-05,-1.05E-05,172.97937,0.0019,-0.0019,0.762,0.139,-0.532,3.1402,0.0673,-0.0673,686,18.7,-18.7,2.77,0.9,-0.3,1160,427.65,420.33,-136.7,40.2,2,6046,189,-232,4.486,0.054,-0.229,0.972,0.315,-0.105,296.28613,48.22467,15.714
-CONFIRMED,0,0,0,0,2.56658897,1.78E-05,-1.78E-05,179.55437,0.00461,-0.00461,0.755,0.212,-0.523,2.429,0.165,-0.165,226.5,16.8,-16.8,1.59,0.52,-0.17,1360,807.74,793.91,-258.2,15,3,6046,189,-232,4.486,0.054,-0.229,0.972,0.315,-0.105,296.28613,48.22467,15.714
-CONFIRMED,0,0,0,0,16.06864674,1.09E-05,-1.09E-05,173.621937,0.000517,-0.000517,0.052,0.262,-0.052,3.5347,0.0241,-0.0241,4914.3,33.3,-33.3,5.76,0.22,-0.49,600,30.75,4.46,-6.66,161.9,1,5031,75,-83,4.485,0.083,-0.028,0.848,0.033,-0.072,286.99948,48.37579,15.841
-CONFIRMED,0,0,0,0,2.470613377,2.70E-08,-2.70E-08,122.763305,8.70E-06,-8.70E-06,0.818,0.001,-0.001,1.74319,0.00107,-0.00107,14231,4.2,-4.2,13.04,0.51,-0.51,1339,761.46,106.21,-95.76,4304.3,1,5820,78,-78,4.457,0.024,-0.024,0.964,0.038,-0.038,286.80847,49.316399,11.338
-CONFIRMED,0,1,0,0,2.204735417,4.30E-08,-4.30E-08,121.3585417,1.60E-05,-1.60E-05,0.224,0.159,-0.216,3.88864,0.00203,-0.00203,6674.7,1.7,-1.7,16.1,0.81,-0.91,2048,4148.92,651.59,-654.46,5945.9,1,6440,76,-89,4.019,0.033,-0.027,1.952,0.099,-0.11,292.24728,47.969521,10.463
-CONFIRMED,0,0,0,0,3.522498429,1.98E-07,-1.98E-07,121.1194228,4.71E-05,-4.71E-05,0.631,0.007,-0.007,3.19843,0.00653,-0.00653,9145.7,6.6,-6.6,14.59,1.11,-1.11,1521,1264.67,307.23,-276.48,1741.5,1,6225,112,-137,4.169,0.055,-0.045,1.451,0.11,-0.11,281.28812,42.45108,13.563
-CONFIRMED,0,0,0,0,3.709214104,6.54E-06,-6.54E-06,133.98318,0.00143,-0.00143,0.051,0.395,-0.051,2.6302,0.0427,-0.0427,131.1,3,-3,1.16,0.17,-0.12,1206,500.46,197.96,-130.54,50.6,2,5833,105,-117,4.407,0.085,-0.114,1.022,0.143,-0.107,295.64871,48.49556,12.772
-FALSE POSITIVE,0,1,0,0,11.52144606,1.98E-06,-1.98E-06,170.839688,0.000131,-0.000131,2.483,2.851,-0.673,3.6399,0.0114,-0.0114,17984,31.9,-31.9,150.51,39.76,-13.31,753,75.88,58.89,-19.99,622.1,1,5795,155,-172,4.554,0.033,-0.176,0.848,0.224,-0.075,297.07993,47.597401,15.472
-FALSE POSITIVE,0,1,0,0,19.22138894,1.12E-06,-1.12E-06,184.5521637,4.50E-05,-4.50E-05,1.065,0.031,-0.034,4.79843,0.00235,-0.00235,74284,21.9,-21.9,49.29,16.03,-5,698,55.97,54.55,-16.4,2317,1,6117,182,-200,4.496,0.052,-0.208,0.947,0.308,-0.096,295.81454,47.69035,15.341
-FALSE POSITIVE,0,1,0,0,16.46983774,1.36E-05,-1.36E-05,180.881761,0.000623,-0.000623,0.292,0.118,-0.101,9.4378,0.06,-0.06,10479,39.9,-39.9,7.94,0.89,-0.89,595,29.61,12.01,-8.91,303.4,1,5152,168,-153,4.517,0.088,-0.072,0.786,0.088,-0.088,297.15442,47.668701,15.788
-CONFIRMED,0,0,0,0,9.27358173,1.04E-05,-1.04E-05,173.258155,0.000877,-0.000877,0.387,0.004,-0.386,3.2875,0.0309,-0.0309,1288.3,16.8,-16.8,2.47,0.2,-0.24,649,41.85,12.49,-11.7,87.2,1,4856,131,-146,4.583,0.065,-0.035,0.696,0.056,-0.068,288.13824,47.724449,15.302
-CONFIRMED,0,0,0,0,6.02930329,5.51E-06,-5.51E-06,171.602959,0.000713,-0.000713,0.258,0.196,-0.258,1.5821,0.0311,-0.0311,1912.7,34.4,-34.4,2.85,0.26,-0.15,678,50.04,16.4,-9.88,65.4,1,4537,123,-123,4.648,0.02,-0.056,0.672,0.062,-0.036,283.71088,47.86327,15.784
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