model_A_svc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: sklearn in c:\\users\\vicki\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn in c:\\users\\vicki\\anaconda3\\lib\\site-packages (from sklearn) (0.22.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.11.0 in c:\\users\\vicki\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.18.1)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\vicki\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.14.1)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.17.0 in c:\\users\\vicki\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.4.1)\n"
     ]
    }
   ],
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the CSV and Perform Basic Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <th>koi_steff_err2</th>\n",
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       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
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       "      <td>-2.630000e-07</td>\n",
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       "      <td>...</td>\n",
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       "<p>5 rows × 41 columns</p>\n",
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      "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": [
    "df = pd.read_csv(\"exoplanet_data.csv\")\n",
    "# Drop the null columns where all values are null\n",
    "df = df.dropna(axis='columns', how='all')\n",
    "# Drop the null rows\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6991 entries, 0 to 6990\n",
      "Data columns (total 41 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   koi_disposition    6991 non-null   object \n",
      " 1   koi_fpflag_nt      6991 non-null   int64  \n",
      " 2   koi_fpflag_ss      6991 non-null   int64  \n",
      " 3   koi_fpflag_co      6991 non-null   int64  \n",
      " 4   koi_fpflag_ec      6991 non-null   int64  \n",
      " 5   koi_period         6991 non-null   float64\n",
      " 6   koi_period_err1    6991 non-null   float64\n",
      " 7   koi_period_err2    6991 non-null   float64\n",
      " 8   koi_time0bk        6991 non-null   float64\n",
      " 9   koi_time0bk_err1   6991 non-null   float64\n",
      " 10  koi_time0bk_err2   6991 non-null   float64\n",
      " 11  koi_impact         6991 non-null   float64\n",
      " 12  koi_impact_err1    6991 non-null   float64\n",
      " 13  koi_impact_err2    6991 non-null   float64\n",
      " 14  koi_duration       6991 non-null   float64\n",
      " 15  koi_duration_err1  6991 non-null   float64\n",
      " 16  koi_duration_err2  6991 non-null   float64\n",
      " 17  koi_depth          6991 non-null   float64\n",
      " 18  koi_depth_err1     6991 non-null   float64\n",
      " 19  koi_depth_err2     6991 non-null   float64\n",
      " 20  koi_prad           6991 non-null   float64\n",
      " 21  koi_prad_err1      6991 non-null   float64\n",
      " 22  koi_prad_err2      6991 non-null   float64\n",
      " 23  koi_teq            6991 non-null   int64  \n",
      " 24  koi_insol          6991 non-null   float64\n",
      " 25  koi_insol_err1     6991 non-null   float64\n",
      " 26  koi_insol_err2     6991 non-null   float64\n",
      " 27  koi_model_snr      6991 non-null   float64\n",
      " 28  koi_tce_plnt_num   6991 non-null   int64  \n",
      " 29  koi_steff          6991 non-null   int64  \n",
      " 30  koi_steff_err1     6991 non-null   int64  \n",
      " 31  koi_steff_err2     6991 non-null   int64  \n",
      " 32  koi_slogg          6991 non-null   float64\n",
      " 33  koi_slogg_err1     6991 non-null   float64\n",
      " 34  koi_slogg_err2     6991 non-null   float64\n",
      " 35  koi_srad           6991 non-null   float64\n",
      " 36  koi_srad_err1      6991 non-null   float64\n",
      " 37  koi_srad_err2      6991 non-null   float64\n",
      " 38  ra                 6991 non-null   float64\n",
      " 39  dec                6991 non-null   float64\n",
      " 40  koi_kepmag         6991 non-null   float64\n",
      "dtypes: float64(31), int64(9), object(1)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Select your features (columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "\n",
    "# Set features. This will also be used as your x values.\n",
    "selected_features = df.drop(\"koi_disposition\", axis=1)\n",
    "X = selected_features"
   ]
  },
  {
   "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": [
    "y = df[\"koi_disposition\"].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6991, 40) (6991, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label_encoder = LabelEncoder()\n",
    "# label_encoder.fit(y_train)\n",
    "# encoded_y_train = label_encoder.transform(y_train)\n",
    "# encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train_categorical = to_categorical(encoded_y_train)\n",
    "#y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
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       "      <th>ra</th>\n",
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       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "\n",
    "Scale the data using the MinMaxScaler and perform some feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale your data\n",
    "# see above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC(kernel='linear')\n",
    "model.fit(X_train_scaled, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model2 = SVC(kernel='linear')\n",
    "#model2.fit(X_test_scaled, np.ravel(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.8373068853709709\n",
      "Testing Data Score: 0.8558352402745996\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score: {model.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score: {model.score(X_test_scaled, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Tuning\n",
    "\n",
    "Use `GridSearchCV` to tune the model's parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [1, 200, 400, 800],\n",
    "              'gamma': [1, 0.0001, 0.005, 0.001]}\n",
    "grid = GridSearchCV(model, param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=1, gamma=1 ....................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........................ C=1, gamma=1, score=0.848, total=   0.4s\n",
      "[CV] C=1, gamma=1 ....................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........................ C=1, gamma=1, score=0.839, total=   0.4s\n",
      "[CV] C=1, gamma=1 ....................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.6s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ........................ C=1, gamma=1, score=0.827, total=   0.4s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................ C=1, gamma=1, score=0.820, total=   0.4s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................ C=1, gamma=1, score=0.832, total=   0.3s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.848, total=   0.3s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.839, total=   0.4s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.827, total=   0.3s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.820, total=   0.4s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ................... C=1, gamma=0.0001, score=0.832, total=   0.3s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.848, total=   0.4s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.839, total=   0.3s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.827, total=   0.3s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.820, total=   0.4s\n",
      "[CV] C=1, gamma=0.005 ................................................\n",
      "[CV] .................... C=1, gamma=0.005, score=0.832, total=   0.4s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.848, total=   0.3s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.839, total=   0.3s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.827, total=   0.3s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.820, total=   0.4s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] .................... C=1, gamma=0.001, score=0.832, total=   0.4s\n",
      "[CV] C=200, gamma=1 ..................................................\n",
      "[CV] ...................... C=200, gamma=1, score=0.887, total=   1.1s\n",
      "[CV] C=200, gamma=1 ..................................................\n",
      "[CV] ...................... C=200, gamma=1, score=0.891, total=   1.3s\n",
      "[CV] C=200, gamma=1 ..................................................\n",
      "[CV] ...................... C=200, gamma=1, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=1 ..................................................\n",
      "[CV] ...................... C=200, gamma=1, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=1 ..................................................\n",
      "[CV] ...................... C=200, gamma=1, score=0.866, total=   0.9s\n",
      "[CV] C=200, gamma=0.0001 .............................................\n",
      "[CV] ................. C=200, gamma=0.0001, score=0.887, total=   1.1s\n",
      "[CV] C=200, gamma=0.0001 .............................................\n",
      "[CV] ................. C=200, gamma=0.0001, score=0.891, total=   1.3s\n",
      "[CV] C=200, gamma=0.0001 .............................................\n",
      "[CV] ................. C=200, gamma=0.0001, score=0.885, total=   1.3s\n",
      "[CV] C=200, gamma=0.0001 .............................................\n",
      "[CV] ................. C=200, gamma=0.0001, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=0.0001 .............................................\n",
      "[CV] ................. C=200, gamma=0.0001, score=0.866, total=   0.9s\n",
      "[CV] C=200, gamma=0.005 ..............................................\n",
      "[CV] .................. C=200, gamma=0.005, score=0.887, total=   1.2s\n",
      "[CV] C=200, gamma=0.005 ..............................................\n",
      "[CV] .................. C=200, gamma=0.005, score=0.891, total=   1.3s\n",
      "[CV] C=200, gamma=0.005 ..............................................\n",
      "[CV] .................. C=200, gamma=0.005, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=0.005 ..............................................\n",
      "[CV] .................. C=200, gamma=0.005, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=0.005 ..............................................\n",
      "[CV] .................. C=200, gamma=0.005, score=0.866, total=   0.9s\n",
      "[CV] C=200, gamma=0.001 ..............................................\n",
      "[CV] .................. C=200, gamma=0.001, score=0.887, total=   1.2s\n",
      "[CV] C=200, gamma=0.001 ..............................................\n",
      "[CV] .................. C=200, gamma=0.001, score=0.891, total=   1.3s\n",
      "[CV] C=200, gamma=0.001 ..............................................\n",
      "[CV] .................. C=200, gamma=0.001, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=0.001 ..............................................\n",
      "[CV] .................. C=200, gamma=0.001, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=0.001 ..............................................\n",
      "[CV] .................. C=200, gamma=0.001, score=0.866, total=   0.9s\n",
      "[CV] C=400, gamma=1 ..................................................\n",
      "[CV] ...................... C=400, gamma=1, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=1 ..................................................\n",
      "[CV] ...................... C=400, gamma=1, score=0.890, total=   1.8s\n",
      "[CV] C=400, gamma=1 ..................................................\n",
      "[CV] ...................... C=400, gamma=1, score=0.887, total=   1.5s\n",
      "[CV] C=400, gamma=1 ..................................................\n",
      "[CV] ...................... C=400, gamma=1, score=0.884, total=   2.0s\n",
      "[CV] C=400, gamma=1 ..................................................\n",
      "[CV] ...................... C=400, gamma=1, score=0.867, total=   1.6s\n",
      "[CV] C=400, gamma=0.0001 .............................................\n",
      "[CV] ................. C=400, gamma=0.0001, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=0.0001 .............................................\n",
      "[CV] ................. C=400, gamma=0.0001, score=0.890, total=   1.8s\n",
      "[CV] C=400, gamma=0.0001 .............................................\n",
      "[CV] ................. C=400, gamma=0.0001, score=0.887, total=   1.5s\n",
      "[CV] C=400, gamma=0.0001 .............................................\n",
      "[CV] ................. C=400, gamma=0.0001, score=0.884, total=   2.0s\n",
      "[CV] C=400, gamma=0.0001 .............................................\n",
      "[CV] ................. C=400, gamma=0.0001, score=0.867, total=   1.6s\n",
      "[CV] C=400, gamma=0.005 ..............................................\n",
      "[CV] .................. C=400, gamma=0.005, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=0.005 ..............................................\n",
      "[CV] .................. C=400, gamma=0.005, score=0.890, total=   1.8s\n",
      "[CV] C=400, gamma=0.005 ..............................................\n",
      "[CV] .................. C=400, gamma=0.005, score=0.887, total=   1.5s\n",
      "[CV] C=400, gamma=0.005 ..............................................\n",
      "[CV] .................. C=400, gamma=0.005, score=0.884, total=   2.0s\n",
      "[CV] C=400, gamma=0.005 ..............................................\n",
      "[CV] .................. C=400, gamma=0.005, score=0.867, total=   1.6s\n",
      "[CV] C=400, gamma=0.001 ..............................................\n",
      "[CV] .................. C=400, gamma=0.001, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=0.001 ..............................................\n",
      "[CV] .................. C=400, gamma=0.001, score=0.890, total=   1.9s\n",
      "[CV] C=400, gamma=0.001 ..............................................\n",
      "[CV] .................. C=400, gamma=0.001, score=0.887, total=   1.5s\n",
      "[CV] C=400, gamma=0.001 ..............................................\n",
      "[CV] .................. C=400, gamma=0.001, score=0.884, total=   2.2s\n",
      "[CV] C=400, gamma=0.001 ..............................................\n",
      "[CV] .................. C=400, gamma=0.001, score=0.867, total=   1.8s\n",
      "[CV] C=800, gamma=1 ..................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ...................... C=800, gamma=1, score=0.887, total=   1.7s\n",
      "[CV] C=800, gamma=1 ..................................................\n",
      "[CV] ...................... C=800, gamma=1, score=0.890, total=   2.5s\n",
      "[CV] C=800, gamma=1 ..................................................\n",
      "[CV] ...................... C=800, gamma=1, score=0.883, total=   2.3s\n",
      "[CV] C=800, gamma=1 ..................................................\n",
      "[CV] ...................... C=800, gamma=1, score=0.884, total=   3.1s\n",
      "[CV] C=800, gamma=1 ..................................................\n",
      "[CV] ...................... C=800, gamma=1, score=0.869, total=   2.4s\n",
      "[CV] C=800, gamma=0.0001 .............................................\n",
      "[CV] ................. C=800, gamma=0.0001, score=0.887, total=   1.9s\n",
      "[CV] C=800, gamma=0.0001 .............................................\n",
      "[CV] ................. C=800, gamma=0.0001, score=0.890, total=   2.8s\n",
      "[CV] C=800, gamma=0.0001 .............................................\n",
      "[CV] ................. C=800, gamma=0.0001, score=0.883, total=   3.1s\n",
      "[CV] C=800, gamma=0.0001 .............................................\n",
      "[CV] ................. C=800, gamma=0.0001, score=0.884, total=   3.7s\n",
      "[CV] C=800, gamma=0.0001 .............................................\n",
      "[CV] ................. C=800, gamma=0.0001, score=0.869, total=   2.3s\n",
      "[CV] C=800, gamma=0.005 ..............................................\n",
      "[CV] .................. C=800, gamma=0.005, score=0.887, total=   2.0s\n",
      "[CV] C=800, gamma=0.005 ..............................................\n",
      "[CV] .................. C=800, gamma=0.005, score=0.890, total=   2.8s\n",
      "[CV] C=800, gamma=0.005 ..............................................\n",
      "[CV] .................. C=800, gamma=0.005, score=0.883, total=   2.7s\n",
      "[CV] C=800, gamma=0.005 ..............................................\n",
      "[CV] .................. C=800, gamma=0.005, score=0.884, total=   3.4s\n",
      "[CV] C=800, gamma=0.005 ..............................................\n",
      "[CV] .................. C=800, gamma=0.005, score=0.869, total=   2.3s\n",
      "[CV] C=800, gamma=0.001 ..............................................\n",
      "[CV] .................. C=800, gamma=0.001, score=0.887, total=   2.8s\n",
      "[CV] C=800, gamma=0.001 ..............................................\n",
      "[CV] .................. C=800, gamma=0.001, score=0.890, total=   2.9s\n",
      "[CV] C=800, gamma=0.001 ..............................................\n",
      "[CV] .................. C=800, gamma=0.001, score=0.883, total=   2.4s\n",
      "[CV] C=800, gamma=0.001 ..............................................\n",
      "[CV] .................. C=800, gamma=0.001, score=0.884, total=   3.1s\n",
      "[CV] C=800, gamma=0.001 ..............................................\n",
      "[CV] .................. C=800, gamma=0.001, score=0.869, total=   2.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:  1.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='linear', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [1, 200, 400, 800],\n",
       "                         'gamma': [1, 0.0001, 0.005, 0.001]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model with GridSearch\n",
    "grid.fit(X_train_scaled, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 400, 'gamma': 1}\n",
      "0.8828886471303095\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NEW MODEL TUNED BASED ON VALUES FROM GRIDSEARCH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=400, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=1, kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = SVC(kernel='linear', C=400, gamma=1)\n",
    "model2.fit(X_train_scaled, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score for model2: 0.8863246233072668\n",
      "Testing Data Score for model2: 0.9016018306636155\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score for model2: {model2.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score for model2: {model2.score(X_test_scaled, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid2 = [{'kernel': ['rbf'],'C': [200, 300, 400, 500],\n",
    "              'gamma': [1, 0.0001, 0.1, 0.001]}, {'kernel': ['linear'],'C': [200, 300, 400, 500],\n",
    "              'gamma': [1, 0.0001, 0.1, 0.001]}]\n",
    "grid2 = GridSearchCV(model, param_grid2, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
      "[CV] C=200, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=200, gamma=1, kernel=rbf, score=0.880, total=   0.6s\n",
      "[CV] C=200, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=200, gamma=1, kernel=rbf, score=0.896, total=   0.6s\n",
      "[CV] C=200, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.2s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=200, gamma=1, kernel=rbf, score=0.874, total=   0.6s\n",
      "[CV] C=200, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=200, gamma=1, kernel=rbf, score=0.884, total=   0.6s\n",
      "[CV] C=200, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=200, gamma=1, kernel=rbf, score=0.881, total=   0.6s\n",
      "[CV] C=200, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=200, gamma=0.0001, kernel=rbf, score=0.800, total=   0.7s\n",
      "[CV] C=200, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=200, gamma=0.0001, kernel=rbf, score=0.804, total=   0.7s\n",
      "[CV] C=200, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=200, gamma=0.0001, kernel=rbf, score=0.794, total=   0.7s\n",
      "[CV] C=200, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=200, gamma=0.0001, kernel=rbf, score=0.781, total=   0.7s\n",
      "[CV] C=200, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=200, gamma=0.0001, kernel=rbf, score=0.793, total=   0.7s\n",
      "[CV] C=200, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=200, gamma=0.1, kernel=rbf, score=0.881, total=   0.5s\n",
      "[CV] C=200, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=200, gamma=0.1, kernel=rbf, score=0.881, total=   0.5s\n",
      "[CV] C=200, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=200, gamma=0.1, kernel=rbf, score=0.867, total=   0.5s\n",
      "[CV] C=200, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=200, gamma=0.1, kernel=rbf, score=0.875, total=   0.5s\n",
      "[CV] C=200, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=200, gamma=0.1, kernel=rbf, score=0.866, total=   0.5s\n",
      "[CV] C=200, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=200, gamma=0.001, kernel=rbf, score=0.837, total=   0.6s\n",
      "[CV] C=200, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=200, gamma=0.001, kernel=rbf, score=0.827, total=   0.6s\n",
      "[CV] C=200, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=200, gamma=0.001, kernel=rbf, score=0.813, total=   0.6s\n",
      "[CV] C=200, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=200, gamma=0.001, kernel=rbf, score=0.806, total=   0.6s\n",
      "[CV] C=200, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=200, gamma=0.001, kernel=rbf, score=0.814, total=   0.6s\n",
      "[CV] C=300, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=300, gamma=1, kernel=rbf, score=0.874, total=   0.7s\n",
      "[CV] C=300, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=300, gamma=1, kernel=rbf, score=0.888, total=   0.7s\n",
      "[CV] C=300, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=300, gamma=1, kernel=rbf, score=0.869, total=   0.7s\n",
      "[CV] C=300, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=300, gamma=1, kernel=rbf, score=0.880, total=   0.7s\n",
      "[CV] C=300, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=300, gamma=1, kernel=rbf, score=0.878, total=   0.7s\n",
      "[CV] C=300, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=300, gamma=0.0001, kernel=rbf, score=0.813, total=   0.6s\n",
      "[CV] C=300, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=300, gamma=0.0001, kernel=rbf, score=0.818, total=   0.6s\n",
      "[CV] C=300, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=300, gamma=0.0001, kernel=rbf, score=0.799, total=   0.7s\n",
      "[CV] C=300, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=300, gamma=0.0001, kernel=rbf, score=0.786, total=   0.6s\n",
      "[CV] C=300, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=300, gamma=0.0001, kernel=rbf, score=0.802, total=   0.6s\n",
      "[CV] C=300, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=300, gamma=0.1, kernel=rbf, score=0.881, total=   0.6s\n",
      "[CV] C=300, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=300, gamma=0.1, kernel=rbf, score=0.884, total=   0.5s\n",
      "[CV] C=300, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=300, gamma=0.1, kernel=rbf, score=0.868, total=   0.5s\n",
      "[CV] C=300, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=300, gamma=0.1, kernel=rbf, score=0.878, total=   0.5s\n",
      "[CV] C=300, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=300, gamma=0.1, kernel=rbf, score=0.865, total=   0.5s\n",
      "[CV] C=300, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=300, gamma=0.001, kernel=rbf, score=0.840, total=   0.6s\n",
      "[CV] C=300, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=300, gamma=0.001, kernel=rbf, score=0.829, total=   0.6s\n",
      "[CV] C=300, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=300, gamma=0.001, kernel=rbf, score=0.817, total=   0.6s\n",
      "[CV] C=300, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=300, gamma=0.001, kernel=rbf, score=0.811, total=   0.6s\n",
      "[CV] C=300, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=300, gamma=0.001, kernel=rbf, score=0.821, total=   0.5s\n",
      "[CV] C=400, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=400, gamma=1, kernel=rbf, score=0.876, total=   0.8s\n",
      "[CV] C=400, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=400, gamma=1, kernel=rbf, score=0.886, total=   0.8s\n",
      "[CV] C=400, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=400, gamma=1, kernel=rbf, score=0.866, total=   0.8s\n",
      "[CV] C=400, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=400, gamma=1, kernel=rbf, score=0.879, total=   0.7s\n",
      "[CV] C=400, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=400, gamma=1, kernel=rbf, score=0.878, total=   0.7s\n",
      "[CV] C=400, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=400, gamma=0.0001, kernel=rbf, score=0.823, total=   0.6s\n",
      "[CV] C=400, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=400, gamma=0.0001, kernel=rbf, score=0.827, total=   0.6s\n",
      "[CV] C=400, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=400, gamma=0.0001, kernel=rbf, score=0.805, total=   0.6s\n",
      "[CV] C=400, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=400, gamma=0.0001, kernel=rbf, score=0.791, total=   0.6s\n",
      "[CV] C=400, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=400, gamma=0.0001, kernel=rbf, score=0.803, total=   0.6s\n",
      "[CV] C=400, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=400, gamma=0.1, kernel=rbf, score=0.882, total=   0.5s\n",
      "[CV] C=400, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=400, gamma=0.1, kernel=rbf, score=0.886, total=   0.5s\n",
      "[CV] C=400, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=400, gamma=0.1, kernel=rbf, score=0.870, total=   0.5s\n",
      "[CV] C=400, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=400, gamma=0.1, kernel=rbf, score=0.880, total=   0.5s\n",
      "[CV] C=400, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=400, gamma=0.1, kernel=rbf, score=0.868, total=   0.5s\n",
      "[CV] C=400, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=400, gamma=0.001, kernel=rbf, score=0.846, total=   0.6s\n",
      "[CV] C=400, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=400, gamma=0.001, kernel=rbf, score=0.835, total=   0.6s\n",
      "[CV] C=400, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=400, gamma=0.001, kernel=rbf, score=0.820, total=   0.6s\n",
      "[CV] C=400, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=400, gamma=0.001, kernel=rbf, score=0.817, total=   0.6s\n",
      "[CV] C=400, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=400, gamma=0.001, kernel=rbf, score=0.828, total=   0.6s\n",
      "[CV] C=500, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .......... C=500, gamma=1, kernel=rbf, score=0.875, total=   0.9s\n",
      "[CV] C=500, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=500, gamma=1, kernel=rbf, score=0.884, total=   0.8s\n",
      "[CV] C=500, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=500, gamma=1, kernel=rbf, score=0.867, total=   0.8s\n",
      "[CV] C=500, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=500, gamma=1, kernel=rbf, score=0.877, total=   0.9s\n",
      "[CV] C=500, gamma=1, kernel=rbf ......................................\n",
      "[CV] .......... C=500, gamma=1, kernel=rbf, score=0.877, total=   0.9s\n",
      "[CV] C=500, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=500, gamma=0.0001, kernel=rbf, score=0.827, total=   0.6s\n",
      "[CV] C=500, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=500, gamma=0.0001, kernel=rbf, score=0.833, total=   0.6s\n",
      "[CV] C=500, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=500, gamma=0.0001, kernel=rbf, score=0.806, total=   0.6s\n",
      "[CV] C=500, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=500, gamma=0.0001, kernel=rbf, score=0.795, total=   0.6s\n",
      "[CV] C=500, gamma=0.0001, kernel=rbf .................................\n",
      "[CV] ..... C=500, gamma=0.0001, kernel=rbf, score=0.804, total=   0.6s\n",
      "[CV] C=500, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=500, gamma=0.1, kernel=rbf, score=0.888, total=   0.5s\n",
      "[CV] C=500, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=500, gamma=0.1, kernel=rbf, score=0.886, total=   0.5s\n",
      "[CV] C=500, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=500, gamma=0.1, kernel=rbf, score=0.868, total=   0.5s\n",
      "[CV] C=500, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=500, gamma=0.1, kernel=rbf, score=0.881, total=   0.5s\n",
      "[CV] C=500, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ........ C=500, gamma=0.1, kernel=rbf, score=0.867, total=   0.5s\n",
      "[CV] C=500, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=500, gamma=0.001, kernel=rbf, score=0.849, total=   0.6s\n",
      "[CV] C=500, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=500, gamma=0.001, kernel=rbf, score=0.839, total=   0.6s\n",
      "[CV] C=500, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=500, gamma=0.001, kernel=rbf, score=0.827, total=   0.6s\n",
      "[CV] C=500, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=500, gamma=0.001, kernel=rbf, score=0.820, total=   0.6s\n",
      "[CV] C=500, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ...... C=500, gamma=0.001, kernel=rbf, score=0.832, total=   0.6s\n",
      "[CV] C=200, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=200, gamma=1, kernel=linear, score=0.887, total=   1.1s\n",
      "[CV] C=200, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=200, gamma=1, kernel=linear, score=0.891, total=   1.3s\n",
      "[CV] C=200, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=200, gamma=1, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=200, gamma=1, kernel=linear, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=200, gamma=1, kernel=linear, score=0.866, total=   0.9s\n",
      "[CV] C=200, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=200, gamma=0.0001, kernel=linear, score=0.887, total=   1.2s\n",
      "[CV] C=200, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=200, gamma=0.0001, kernel=linear, score=0.891, total=   1.4s\n",
      "[CV] C=200, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=200, gamma=0.0001, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=200, gamma=0.0001, kernel=linear, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=200, gamma=0.0001, kernel=linear, score=0.866, total=   0.9s\n",
      "[CV] C=200, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=200, gamma=0.1, kernel=linear, score=0.887, total=   1.2s\n",
      "[CV] C=200, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=200, gamma=0.1, kernel=linear, score=0.891, total=   1.4s\n",
      "[CV] C=200, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=200, gamma=0.1, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=200, gamma=0.1, kernel=linear, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=200, gamma=0.1, kernel=linear, score=0.866, total=   0.9s\n",
      "[CV] C=200, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=200, gamma=0.001, kernel=linear, score=0.887, total=   1.2s\n",
      "[CV] C=200, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=200, gamma=0.001, kernel=linear, score=0.891, total=   1.4s\n",
      "[CV] C=200, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=200, gamma=0.001, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=200, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=200, gamma=0.001, kernel=linear, score=0.882, total=   1.1s\n",
      "[CV] C=200, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=200, gamma=0.001, kernel=linear, score=0.866, total=   0.9s\n",
      "[CV] C=300, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=300, gamma=1, kernel=linear, score=0.888, total=   1.3s\n",
      "[CV] C=300, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=300, gamma=1, kernel=linear, score=0.891, total=   1.3s\n",
      "[CV] C=300, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=300, gamma=1, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=300, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=300, gamma=1, kernel=linear, score=0.882, total=   1.7s\n",
      "[CV] C=300, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=300, gamma=1, kernel=linear, score=0.867, total=   1.4s\n",
      "[CV] C=300, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=300, gamma=0.0001, kernel=linear, score=0.888, total=   1.3s\n",
      "[CV] C=300, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=300, gamma=0.0001, kernel=linear, score=0.891, total=   1.3s\n",
      "[CV] C=300, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=300, gamma=0.0001, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=300, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=300, gamma=0.0001, kernel=linear, score=0.882, total=   1.7s\n",
      "[CV] C=300, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=300, gamma=0.0001, kernel=linear, score=0.867, total=   1.4s\n",
      "[CV] C=300, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=300, gamma=0.1, kernel=linear, score=0.888, total=   1.3s\n",
      "[CV] C=300, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=300, gamma=0.1, kernel=linear, score=0.891, total=   1.3s\n",
      "[CV] C=300, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=300, gamma=0.1, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=300, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=300, gamma=0.1, kernel=linear, score=0.882, total=   1.7s\n",
      "[CV] C=300, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=300, gamma=0.1, kernel=linear, score=0.867, total=   1.4s\n",
      "[CV] C=300, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=300, gamma=0.001, kernel=linear, score=0.888, total=   1.3s\n",
      "[CV] C=300, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=300, gamma=0.001, kernel=linear, score=0.891, total=   1.3s\n",
      "[CV] C=300, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=300, gamma=0.001, kernel=linear, score=0.885, total=   1.4s\n",
      "[CV] C=300, gamma=0.001, kernel=linear ...............................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ... C=300, gamma=0.001, kernel=linear, score=0.882, total=   1.7s\n",
      "[CV] C=300, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=300, gamma=0.001, kernel=linear, score=0.867, total=   1.4s\n",
      "[CV] C=400, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=400, gamma=1, kernel=linear, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=400, gamma=1, kernel=linear, score=0.890, total=   1.9s\n",
      "[CV] C=400, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=400, gamma=1, kernel=linear, score=0.887, total=   1.5s\n",
      "[CV] C=400, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=400, gamma=1, kernel=linear, score=0.884, total=   2.0s\n",
      "[CV] C=400, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=400, gamma=1, kernel=linear, score=0.867, total=   1.6s\n",
      "[CV] C=400, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=400, gamma=0.0001, kernel=linear, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=400, gamma=0.0001, kernel=linear, score=0.890, total=   1.8s\n",
      "[CV] C=400, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=400, gamma=0.0001, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=400, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=400, gamma=0.0001, kernel=linear, score=0.884, total=   2.0s\n",
      "[CV] C=400, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=400, gamma=0.0001, kernel=linear, score=0.867, total=   1.6s\n",
      "[CV] C=400, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=400, gamma=0.1, kernel=linear, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=400, gamma=0.1, kernel=linear, score=0.890, total=   1.9s\n",
      "[CV] C=400, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=400, gamma=0.1, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=400, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=400, gamma=0.1, kernel=linear, score=0.884, total=   2.1s\n",
      "[CV] C=400, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=400, gamma=0.1, kernel=linear, score=0.867, total=   1.6s\n",
      "[CV] C=400, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=400, gamma=0.001, kernel=linear, score=0.887, total=   1.3s\n",
      "[CV] C=400, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=400, gamma=0.001, kernel=linear, score=0.890, total=   1.9s\n",
      "[CV] C=400, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=400, gamma=0.001, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=400, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=400, gamma=0.001, kernel=linear, score=0.884, total=   2.0s\n",
      "[CV] C=400, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=400, gamma=0.001, kernel=linear, score=0.867, total=   1.6s\n",
      "[CV] C=500, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=500, gamma=1, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=500, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=500, gamma=1, kernel=linear, score=0.891, total=   2.0s\n",
      "[CV] C=500, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=500, gamma=1, kernel=linear, score=0.884, total=   1.8s\n",
      "[CV] C=500, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=500, gamma=1, kernel=linear, score=0.883, total=   2.0s\n",
      "[CV] C=500, gamma=1, kernel=linear ...................................\n",
      "[CV] ....... C=500, gamma=1, kernel=linear, score=0.868, total=   1.6s\n",
      "[CV] C=500, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=500, gamma=0.0001, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=500, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=500, gamma=0.0001, kernel=linear, score=0.891, total=   2.0s\n",
      "[CV] C=500, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=500, gamma=0.0001, kernel=linear, score=0.884, total=   1.8s\n",
      "[CV] C=500, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=500, gamma=0.0001, kernel=linear, score=0.883, total=   2.1s\n",
      "[CV] C=500, gamma=0.0001, kernel=linear ..............................\n",
      "[CV] .. C=500, gamma=0.0001, kernel=linear, score=0.868, total=   1.6s\n",
      "[CV] C=500, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=500, gamma=0.1, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=500, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=500, gamma=0.1, kernel=linear, score=0.891, total=   2.0s\n",
      "[CV] C=500, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=500, gamma=0.1, kernel=linear, score=0.884, total=   1.7s\n",
      "[CV] C=500, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=500, gamma=0.1, kernel=linear, score=0.883, total=   2.0s\n",
      "[CV] C=500, gamma=0.1, kernel=linear .................................\n",
      "[CV] ..... C=500, gamma=0.1, kernel=linear, score=0.868, total=   1.6s\n",
      "[CV] C=500, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=500, gamma=0.001, kernel=linear, score=0.887, total=   1.6s\n",
      "[CV] C=500, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=500, gamma=0.001, kernel=linear, score=0.891, total=   2.0s\n",
      "[CV] C=500, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=500, gamma=0.001, kernel=linear, score=0.884, total=   1.7s\n",
      "[CV] C=500, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=500, gamma=0.001, kernel=linear, score=0.883, total=   2.0s\n",
      "[CV] C=500, gamma=0.001, kernel=linear ...............................\n",
      "[CV] ... C=500, gamma=0.001, kernel=linear, score=0.868, total=   1.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 160 out of 160 | elapsed:  2.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='linear', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'C': [200, 300, 400, 500],\n",
       "                          'gamma': [1, 0.0001, 0.1, 0.001], 'kernel': ['rbf']},\n",
       "                         {'C': [200, 300, 400, 500],\n",
       "                          'gamma': [1, 0.0001, 0.1, 0.001],\n",
       "                          'kernel': ['linear']}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid2.fit(X_train_scaled, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 200, 'gamma': 1, 'kernel': 'rbf'}\n",
      "0.8828911940852429\n"
     ]
    }
   ],
   "source": [
    "print(grid2.best_params_)\n",
    "print(grid2.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ANOTHER NEW MODEL(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=200, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
       "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3 = SVC(kernel='rbf', C=200, gamma=1)\n",
    "model3.fit(X_train_scaled, np.ravel(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score for model3: 0.9158878504672897\n",
      "Testing Data Score for model3: 0.8924485125858124\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score for model3: {model3.score(X_train_scaled, y_train)}\")\n",
    "print(f\"Testing Data Score for model3: {model3.score(X_test_scaled, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jloucks_SVC1.sav']"
      ]
     },
     "execution_count": 40,
     "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 = 'jloucks_SVC1.sav'\n",
    "joblib.dump(model2, filename)"
   ]
  }
 ],
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