exoplanets_KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update sklearn to prevent version mismatches\n",
    "!pip install sklearn --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# install joblib. This will be used to save your model. \n",
    "# Restart your kernel after installing \n",
    "!pip install joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore', FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_disposition</th>\n",
       "      <th>koi_fpflag_nt</th>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
       "      <th>koi_period</th>\n",
       "      <th>koi_period_err1</th>\n",
       "      <th>koi_period_err2</th>\n",
       "      <th>koi_time0bk</th>\n",
       "      <th>koi_time0bk_err1</th>\n",
       "      <th>...</th>\n",
       "      <th>koi_steff_err2</th>\n",
       "      <th>koi_slogg</th>\n",
       "      <th>koi_slogg_err1</th>\n",
       "      <th>koi_slogg_err2</th>\n",
       "      <th>koi_srad</th>\n",
       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>koi_kepmag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.418383</td>\n",
       "      <td>2.479000e-04</td>\n",
       "      <td>-2.479000e-04</td>\n",
       "      <td>162.513840</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>...</td>\n",
       "      <td>-81</td>\n",
       "      <td>4.467</td>\n",
       "      <td>0.064</td>\n",
       "      <td>-0.096</td>\n",
       "      <td>0.927</td>\n",
       "      <td>0.105</td>\n",
       "      <td>-0.061</td>\n",
       "      <td>291.93423</td>\n",
       "      <td>48.141651</td>\n",
       "      <td>15.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.899140</td>\n",
       "      <td>1.490000e-05</td>\n",
       "      <td>-1.490000e-05</td>\n",
       "      <td>175.850252</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>...</td>\n",
       "      <td>-176</td>\n",
       "      <td>4.544</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.176</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.233</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>297.00482</td>\n",
       "      <td>48.134129</td>\n",
       "      <td>15.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FALSE POSITIVE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.736952</td>\n",
       "      <td>2.630000e-07</td>\n",
       "      <td>-2.630000e-07</td>\n",
       "      <td>170.307565</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>...</td>\n",
       "      <td>-174</td>\n",
       "      <td>4.564</td>\n",
       "      <td>0.053</td>\n",
       "      <td>-0.168</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.201</td>\n",
       "      <td>-0.067</td>\n",
       "      <td>285.53461</td>\n",
       "      <td>48.285210</td>\n",
       "      <td>15.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.525592</td>\n",
       "      <td>3.760000e-06</td>\n",
       "      <td>-3.760000e-06</td>\n",
       "      <td>171.595550</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>...</td>\n",
       "      <td>-211</td>\n",
       "      <td>4.438</td>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.210</td>\n",
       "      <td>1.046</td>\n",
       "      <td>0.334</td>\n",
       "      <td>-0.133</td>\n",
       "      <td>288.75488</td>\n",
       "      <td>48.226200</td>\n",
       "      <td>15.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CONFIRMED</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.134435</td>\n",
       "      <td>1.050000e-05</td>\n",
       "      <td>-1.050000e-05</td>\n",
       "      <td>172.979370</td>\n",
       "      <td>0.001900</td>\n",
       "      <td>...</td>\n",
       "      <td>-232</td>\n",
       "      <td>4.486</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.229</td>\n",
       "      <td>0.972</td>\n",
       "      <td>0.315</td>\n",
       "      <td>-0.105</td>\n",
       "      <td>296.28613</td>\n",
       "      <td>48.224670</td>\n",
       "      <td>15.714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  koi_disposition  koi_fpflag_nt  koi_fpflag_ss  koi_fpflag_co  koi_fpflag_ec  \\\n",
       "0       CONFIRMED              0              0              0              0   \n",
       "1  FALSE POSITIVE              0              1              0              0   \n",
       "2  FALSE POSITIVE              0              1              0              0   \n",
       "3       CONFIRMED              0              0              0              0   \n",
       "4       CONFIRMED              0              0              0              0   \n",
       "\n",
       "   koi_period  koi_period_err1  koi_period_err2  koi_time0bk  \\\n",
       "0   54.418383     2.479000e-04    -2.479000e-04   162.513840   \n",
       "1   19.899140     1.490000e-05    -1.490000e-05   175.850252   \n",
       "2    1.736952     2.630000e-07    -2.630000e-07   170.307565   \n",
       "3    2.525592     3.760000e-06    -3.760000e-06   171.595550   \n",
       "4    4.134435     1.050000e-05    -1.050000e-05   172.979370   \n",
       "\n",
       "   koi_time0bk_err1  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "0          0.003520  ...             -81      4.467           0.064   \n",
       "1          0.000581  ...            -176      4.544           0.044   \n",
       "2          0.000115  ...            -174      4.564           0.053   \n",
       "3          0.001130  ...            -211      4.438           0.070   \n",
       "4          0.001900  ...            -232      4.486           0.054   \n",
       "\n",
       "   koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "0          -0.096     0.927          0.105         -0.061  291.93423   \n",
       "1          -0.176     0.868          0.233         -0.078  297.00482   \n",
       "2          -0.168     0.791          0.201         -0.067  285.53461   \n",
       "3          -0.210     1.046          0.334         -0.133  288.75488   \n",
       "4          -0.229     0.972          0.315         -0.105  296.28613   \n",
       "\n",
       "         dec  koi_kepmag  \n",
       "0  48.141651      15.347  \n",
       "1  48.134129      15.436  \n",
       "2  48.285210      15.597  \n",
       "3  48.226200      15.509  \n",
       "4  48.224670      15.714  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/exoplanet_data.csv\")\n",
    "# Drop the null columns where all values are null\n",
    "df = df.dropna(axis='columns', how='all')\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set features. This will also be used as your x values.\n",
    "selected_features = df.drop(\"koi_disposition\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Train Test Split\n",
    "\n",
    "Use `koi_disposition` for the y values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6991, 40) (6991,)\n"
     ]
    }
   ],
   "source": [
    "y = df[\"koi_disposition\"]\n",
    "print(selected_features.shape, y.shape)"
   ]
  },
  {
   "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>3563</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.548413</td>\n",
       "      <td>5.470000e-05</td>\n",
       "      <td>-5.470000e-05</td>\n",
       "      <td>139.064020</td>\n",
       "      <td>0.004110</td>\n",
       "      <td>-0.004110</td>\n",
       "      <td>...</td>\n",
       "      <td>-133</td>\n",
       "      <td>4.387</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.123</td>\n",
       "      <td>1.092</td>\n",
       "      <td>0.181</td>\n",
       "      <td>-0.097</td>\n",
       "      <td>298.09543</td>\n",
       "      <td>44.737061</td>\n",
       "      <td>13.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4099</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.754385</td>\n",
       "      <td>1.365000e-04</td>\n",
       "      <td>-1.365000e-04</td>\n",
       "      <td>140.207320</td>\n",
       "      <td>0.004460</td>\n",
       "      <td>-0.004460</td>\n",
       "      <td>...</td>\n",
       "      <td>-144</td>\n",
       "      <td>4.519</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.056</td>\n",
       "      <td>-0.076</td>\n",
       "      <td>295.73535</td>\n",
       "      <td>42.576248</td>\n",
       "      <td>15.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5460</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.057336</td>\n",
       "      <td>1.230000e-07</td>\n",
       "      <td>-1.230000e-07</td>\n",
       "      <td>131.792007</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>...</td>\n",
       "      <td>-140</td>\n",
       "      <td>4.594</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.683</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>292.18417</td>\n",
       "      <td>49.310040</td>\n",
       "      <td>15.414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201.118319</td>\n",
       "      <td>1.461000e-03</td>\n",
       "      <td>-1.461000e-03</td>\n",
       "      <td>187.569860</td>\n",
       "      <td>0.005290</td>\n",
       "      <td>-0.005290</td>\n",
       "      <td>...</td>\n",
       "      <td>-112</td>\n",
       "      <td>4.447</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.135</td>\n",
       "      <td>-0.083</td>\n",
       "      <td>283.11377</td>\n",
       "      <td>48.131390</td>\n",
       "      <td>13.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5999</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.649983</td>\n",
       "      <td>3.181000e-03</td>\n",
       "      <td>-3.181000e-03</td>\n",
       "      <td>175.715600</td>\n",
       "      <td>0.028600</td>\n",
       "      <td>-0.028600</td>\n",
       "      <td>...</td>\n",
       "      <td>-233</td>\n",
       "      <td>4.145</td>\n",
       "      <td>0.164</td>\n",
       "      <td>-0.164</td>\n",
       "      <td>1.608</td>\n",
       "      <td>0.905</td>\n",
       "      <td>-0.383</td>\n",
       "      <td>294.93198</td>\n",
       "      <td>39.812420</td>\n",
       "      <td>12.964</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",
       "3563              0              0              0              0   10.548413   \n",
       "4099              0              0              0              0   24.754385   \n",
       "5460              0              0              0              0    1.057336   \n",
       "1091              0              0              0              0  201.118319   \n",
       "5999              0              0              0              0   91.649983   \n",
       "\n",
       "      koi_period_err1  koi_period_err2  koi_time0bk  koi_time0bk_err1  \\\n",
       "3563     5.470000e-05    -5.470000e-05   139.064020          0.004110   \n",
       "4099     1.365000e-04    -1.365000e-04   140.207320          0.004460   \n",
       "5460     1.230000e-07    -1.230000e-07   131.792007          0.000096   \n",
       "1091     1.461000e-03    -1.461000e-03   187.569860          0.005290   \n",
       "5999     3.181000e-03    -3.181000e-03   175.715600          0.028600   \n",
       "\n",
       "      koi_time0bk_err2  ...  koi_steff_err2  koi_slogg  koi_slogg_err1  \\\n",
       "3563         -0.004110  ...            -133      4.387           0.066   \n",
       "4099         -0.004460  ...            -144      4.519           0.078   \n",
       "5460         -0.000096  ...            -140      4.594           0.054   \n",
       "1091         -0.005290  ...            -112      4.447           0.072   \n",
       "5999         -0.028600  ...            -233      4.145           0.164   \n",
       "\n",
       "      koi_slogg_err2  koi_srad  koi_srad_err1  koi_srad_err2         ra  \\\n",
       "3563          -0.123     1.092          0.181         -0.097  298.09543   \n",
       "4099          -0.052     0.804          0.056         -0.076  295.73535   \n",
       "5460          -0.027     0.683          0.054         -0.060  292.18417   \n",
       "1091          -0.108     0.954          0.135         -0.083  283.11377   \n",
       "5999          -0.164     1.608          0.905         -0.383  294.93198   \n",
       "\n",
       "            dec  koi_kepmag  \n",
       "3563  44.737061      13.204  \n",
       "4099  42.576248      15.514  \n",
       "5460  49.310040      15.414  \n",
       "1091  48.131390      13.328  \n",
       "5999  39.812420      12.964  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "X_train, X_test, y_train, y_test = train_test_split(selected_features, y, random_state=1)\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "\n",
    "Scale the data using the MinMaxScaler and perform some feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale your data\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)\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "y_train = label_encoder.transform(y_train)\n",
    "y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K: 1, Train/Test Score: 1.000/0.787\n",
      "K: 3, Train/Test Score: 0.895/0.805\n",
      "K: 5, Train/Test Score: 0.871/0.811\n",
      "K: 7, Train/Test Score: 0.862/0.818\n",
      "K: 9, Train/Test Score: 0.858/0.820\n",
      "K: 11, Train/Test Score: 0.848/0.830\n",
      "K: 13, Train/Test Score: 0.848/0.830\n",
      "K: 15, Train/Test Score: 0.842/0.830\n",
      "K: 17, Train/Test Score: 0.839/0.830\n",
      "K: 19, Train/Test Score: 0.836/0.831\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for k in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, encoded_y_train)\n",
    "    train_score = knn.score(X_train_scaled, y_train)\n",
    "    test_score = knn.score(X_test_scaled, y_test)\n",
    "    train_scores.append(train_score)\n",
    "    test_scores.append(test_score)\n",
    "    print(f\"K: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}\")\n",
    "\n",
    "plt.plot(range(1,20,2), train_scores, marker='o')\n",
    "plt.plot(range(1,20,2), test_scores, marker='x')\n",
    "plt.xlabel('k neighbors')\n",
    "plt.ylabel('Testing accuracy Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 2 ... 2 2 1]\n",
      "k=13 Training Data Score: 0.84818\n",
      "k=13 Testing Data Score: 0.82952\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=13)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "predictions = knn.predict(X_test_scaled)\n",
    "print(predictions)\n",
    "\n",
    "print(f\"k=13 Training Data Score: {knn.score(X_train_scaled, y_train):.5f}\")\n",
    "print(f\"k=13 Testing Data Score: {knn.score(X_test_scaled, y_test):.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                precision    recall  f1-score   support\n",
      "\n",
      "     Confirmed       0.70      0.50      0.58       404\n",
      "False Positive       0.63      0.79      0.70       435\n",
      "     Candidate       0.99      1.00      0.99       909\n",
      "\n",
      "      accuracy                           0.83      1748\n",
      "     macro avg       0.77      0.76      0.76      1748\n",
      "  weighted avg       0.83      0.83      0.82      1748\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predictions, \n",
    "                            target_names = [\"Confirmed\", \"False Positive\", \"Candidate\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['KNN_model.sav']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save your model by updating \"your_name\" with your name\n",
    "# and \"your_model\" with your model variable\n",
    "# be sure to turn this in to BCS\n",
    "# if joblib fails to import, try running the command to install in terminal/git-bash\n",
    "import joblib\n",
    "filename = 'KNN_model.sav'\n",
    "joblib.dump(knn, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "dev"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  },
  "nteract": {
   "version": "0.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}