Case Study 3 MLP-18BCS6201 (AI-ML-2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "#Importing necessary libraries.\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import KFold\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "#Importing 'csv' file as dataframe using 'pd.read_csv()' function.\n",
    "dataset = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000, 785)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning shape of the dataset.\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Specifying value range.\n",
    "y = dataset.iloc[:,0].values\n",
    "x = dataset.iloc[:,1:].values\n",
    "\n",
    "#Converting values to 'float32'\n",
    "x = x.astype('float32')\n",
    "y = y.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "x = sc.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42000,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning shape.\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting a piece of the dataset to work with.\n",
    "x = x[0:12000][:]\n",
    "y = y[0:12000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 784)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning shape.\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting data for training and testing.\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,random_state = 0,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9600, 784)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning shape.\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2400, 784)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning shape.\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Building model.\n",
    "model_linear = SVC(kernel='linear')\n",
    "model_linear.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_linear.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9133333333333333 \n",
      "\n",
      "[[199   0   2   1   1   2   0   0   2   0]\n",
      " [  0 257   1   0   2   0   1   0   1   1]\n",
      " [  0   4 233   3   2   2   1   2   6   1]\n",
      " [  0   2   4 225   0  10   0   2   4   1]\n",
      " [  0   0   1   0 217   0   1   0   0   5]\n",
      " [  2   1   6  15   1 206   2   1   5   1]\n",
      " [  2   0   3   0   2   4 244   0   2   0]\n",
      " [  0   2   3   2   9   0   0 237   0  11]\n",
      " [  3   4   3   9   2   7   4   1 188   2]\n",
      " [  0   1   4   1  13   1   0  12   2 186]]\n"
     ]
    }
   ],
   "source": [
    "#Returning 'accuracy'.\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_true=y_test, y_pred=y_pred), \"\\n\")\n",
    "\n",
    "#Returning 'confusion matrix'.\n",
    "print(metrics.confusion_matrix(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_linear_model = SVC(kernel='rbf')\n",
    "\n",
    "#Fitting the model.\n",
    "non_linear_model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.935 \n",
      "\n",
      "[[195   0   3   0   1   1   4   0   3   0]\n",
      " [  0 259   2   0   1   0   1   0   0   0]\n",
      " [  0   2 236   5   2   0   2   2   5   0]\n",
      " [  0   1   3 233   0   2   0   2   6   1]\n",
      " [  0   0   5   0 214   0   0   3   0   2]\n",
      " [  1   1   2   6   2 223   3   0   1   1]\n",
      " [  2   0   5   0   2   3 243   0   2   0]\n",
      " [  0   1   5   1   6   0   0 245   0   6]\n",
      " [  2   2   6   4   2   2   2   0 201   2]\n",
      " [  2   1   6   2   7   1   0   6   0 195]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = non_linear_model.predict(x_test)\n",
    "\n",
    "#Returning 'accuracy'.\n",
    "print(\"accuracy:\", metrics.accuracy_score(y_true=y_test, y_pred=y_pred), \"\\n\")\n",
    "\n",
    "#Returning 'confusion matrix'.\n",
    "print(metrics.confusion_matrix(y_true=y_test, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done  60 out of  60 | elapsed: 128.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=101, shuffle=True),\n",
       "             estimator=SVC(),\n",
       "             param_grid=[{'C': [1, 10, 100, 1000],\n",
       "                          'gamma': [0.01, 0.001, 0.0001]}],\n",
       "             return_train_score=True, scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#K-fold.\n",
    "folds = KFold(n_splits = 5, shuffle = True, random_state = 101)\n",
    "\n",
    "#Specifying the range of hyperparameters.\n",
    "#Setting the parameters by cross-validation.\n",
    "hyper_params = [ {'gamma': [1e-2, 1e-3, 1e-4],'C': [1, 10, 100, 1000]}]\n",
    "\n",
    "# Specifying model.\n",
    "model = SVC(kernel=\"rbf\")\n",
    "\n",
    "#Setting up 'GridSearchCV()'.\n",
    "model_cv = GridSearchCV(estimator = model,param_grid = hyper_params,scoring= 'accuracy',\n",
    "                        cv = folds,verbose = 1,return_train_score=True)\n",
    "\n",
    "#Fitting the model.\n",
    "model_cv.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_gamma</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200.787921</td>\n",
       "      <td>32.771559</td>\n",
       "      <td>31.397255</td>\n",
       "      <td>10.862396</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 1, 'gamma': 0.01}</td>\n",
       "      <td>0.774479</td>\n",
       "      <td>0.753646</td>\n",
       "      <td>0.744792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.753125</td>\n",
       "      <td>0.011794</td>\n",
       "      <td>12</td>\n",
       "      <td>0.999609</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.999870</td>\n",
       "      <td>0.999740</td>\n",
       "      <td>0.000082</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.958498</td>\n",
       "      <td>22.200390</td>\n",
       "      <td>20.158121</td>\n",
       "      <td>9.293352</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 1, 'gamma': 0.001}</td>\n",
       "      <td>0.932292</td>\n",
       "      <td>0.931250</td>\n",
       "      <td>0.932292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.932396</td>\n",
       "      <td>0.004172</td>\n",
       "      <td>4</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.975651</td>\n",
       "      <td>0.974740</td>\n",
       "      <td>0.975260</td>\n",
       "      <td>0.975391</td>\n",
       "      <td>0.975208</td>\n",
       "      <td>0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.173316</td>\n",
       "      <td>0.838252</td>\n",
       "      <td>11.212399</td>\n",
       "      <td>0.223952</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>{'C': 1, 'gamma': 0.0001}</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.903646</td>\n",
       "      <td>0.906771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907396</td>\n",
       "      <td>0.003080</td>\n",
       "      <td>8</td>\n",
       "      <td>0.920312</td>\n",
       "      <td>0.916797</td>\n",
       "      <td>0.921224</td>\n",
       "      <td>0.918880</td>\n",
       "      <td>0.919271</td>\n",
       "      <td>0.919297</td>\n",
       "      <td>0.001495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>112.698858</td>\n",
       "      <td>1.864177</td>\n",
       "      <td>15.278563</td>\n",
       "      <td>1.202191</td>\n",
       "      <td>10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 10, 'gamma': 0.01}</td>\n",
       "      <td>0.786979</td>\n",
       "      <td>0.772917</td>\n",
       "      <td>0.766146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770729</td>\n",
       "      <td>0.010160</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.857809</td>\n",
       "      <td>0.724440</td>\n",
       "      <td>7.342263</td>\n",
       "      <td>0.350944</td>\n",
       "      <td>10</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 10, 'gamma': 0.001}</td>\n",
       "      <td>0.945312</td>\n",
       "      <td>0.943229</td>\n",
       "      <td>0.943229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944063</td>\n",
       "      <td>0.005002</td>\n",
       "      <td>1</td>\n",
       "      <td>0.998958</td>\n",
       "      <td>0.999349</td>\n",
       "      <td>0.998828</td>\n",
       "      <td>0.998958</td>\n",
       "      <td>0.999219</td>\n",
       "      <td>0.999062</td>\n",
       "      <td>0.000191</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.199067</td>\n",
       "      <td>0.487420</td>\n",
       "      <td>7.068571</td>\n",
       "      <td>0.238040</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>{'C': 10, 'gamma': 0.0001}</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.923438</td>\n",
       "      <td>0.926042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.926979</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>6</td>\n",
       "      <td>0.960677</td>\n",
       "      <td>0.960547</td>\n",
       "      <td>0.959375</td>\n",
       "      <td>0.962240</td>\n",
       "      <td>0.961458</td>\n",
       "      <td>0.960859</td>\n",
       "      <td>0.000959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>121.104463</td>\n",
       "      <td>2.854109</td>\n",
       "      <td>14.967634</td>\n",
       "      <td>0.270723</td>\n",
       "      <td>100</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 100, 'gamma': 0.01}</td>\n",
       "      <td>0.786979</td>\n",
       "      <td>0.772917</td>\n",
       "      <td>0.766146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770729</td>\n",
       "      <td>0.010160</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.911807</td>\n",
       "      <td>1.156844</td>\n",
       "      <td>7.132260</td>\n",
       "      <td>1.207545</td>\n",
       "      <td>100</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 100, 'gamma': 0.001}</td>\n",
       "      <td>0.944792</td>\n",
       "      <td>0.944271</td>\n",
       "      <td>0.942187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.943854</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.593313</td>\n",
       "      <td>3.036818</td>\n",
       "      <td>6.995885</td>\n",
       "      <td>1.553187</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>{'C': 100, 'gamma': 0.0001}</td>\n",
       "      <td>0.930729</td>\n",
       "      <td>0.929167</td>\n",
       "      <td>0.926562</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929479</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.994010</td>\n",
       "      <td>0.994792</td>\n",
       "      <td>0.993490</td>\n",
       "      <td>0.994141</td>\n",
       "      <td>0.992839</td>\n",
       "      <td>0.993854</td>\n",
       "      <td>0.000656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>159.563854</td>\n",
       "      <td>10.244050</td>\n",
       "      <td>18.451927</td>\n",
       "      <td>3.487765</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.01</td>\n",
       "      <td>{'C': 1000, 'gamma': 0.01}</td>\n",
       "      <td>0.786979</td>\n",
       "      <td>0.772917</td>\n",
       "      <td>0.766146</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770729</td>\n",
       "      <td>0.010160</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18.727431</td>\n",
       "      <td>2.171418</td>\n",
       "      <td>6.256812</td>\n",
       "      <td>0.539385</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>{'C': 1000, 'gamma': 0.001}</td>\n",
       "      <td>0.944792</td>\n",
       "      <td>0.944271</td>\n",
       "      <td>0.942187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.943854</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.592129</td>\n",
       "      <td>0.992954</td>\n",
       "      <td>5.148012</td>\n",
       "      <td>0.413785</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>{'C': 1000, 'gamma': 0.0001}</td>\n",
       "      <td>0.928646</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.922917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.924687</td>\n",
       "      <td>0.002668</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0      200.787921     32.771559        31.397255       10.862396       1   \n",
       "1       63.958498     22.200390        20.158121        9.293352       1   \n",
       "2       37.173316      0.838252        11.212399        0.223952       1   \n",
       "3      112.698858      1.864177        15.278563        1.202191      10   \n",
       "4       22.857809      0.724440         7.342263        0.350944      10   \n",
       "5       18.199067      0.487420         7.068571        0.238040      10   \n",
       "6      121.104463      2.854109        14.967634        0.270723     100   \n",
       "7       20.911807      1.156844         7.132260        1.207545     100   \n",
       "8       14.593313      3.036818         6.995885        1.553187     100   \n",
       "9      159.563854     10.244050        18.451927        3.487765    1000   \n",
       "10      18.727431      2.171418         6.256812        0.539385    1000   \n",
       "11      11.592129      0.992954         5.148012        0.413785    1000   \n",
       "\n",
       "   param_gamma                        params  split0_test_score  \\\n",
       "0         0.01       {'C': 1, 'gamma': 0.01}           0.774479   \n",
       "1        0.001      {'C': 1, 'gamma': 0.001}           0.932292   \n",
       "2       0.0001     {'C': 1, 'gamma': 0.0001}           0.906250   \n",
       "3         0.01      {'C': 10, 'gamma': 0.01}           0.786979   \n",
       "4        0.001     {'C': 10, 'gamma': 0.001}           0.945312   \n",
       "5       0.0001    {'C': 10, 'gamma': 0.0001}           0.925000   \n",
       "6         0.01     {'C': 100, 'gamma': 0.01}           0.786979   \n",
       "7        0.001    {'C': 100, 'gamma': 0.001}           0.944792   \n",
       "8       0.0001   {'C': 100, 'gamma': 0.0001}           0.930729   \n",
       "9         0.01    {'C': 1000, 'gamma': 0.01}           0.786979   \n",
       "10       0.001   {'C': 1000, 'gamma': 0.001}           0.944792   \n",
       "11      0.0001  {'C': 1000, 'gamma': 0.0001}           0.928646   \n",
       "\n",
       "    split1_test_score  split2_test_score  ...  mean_test_score  \\\n",
       "0            0.753646           0.744792  ...         0.753125   \n",
       "1            0.931250           0.932292  ...         0.932396   \n",
       "2            0.903646           0.906771  ...         0.907396   \n",
       "3            0.772917           0.766146  ...         0.770729   \n",
       "4            0.943229           0.943229  ...         0.944063   \n",
       "5            0.923438           0.926042  ...         0.926979   \n",
       "6            0.772917           0.766146  ...         0.770729   \n",
       "7            0.944271           0.942187  ...         0.943854   \n",
       "8            0.929167           0.926562  ...         0.929479   \n",
       "9            0.772917           0.766146  ...         0.770729   \n",
       "10           0.944271           0.942187  ...         0.943854   \n",
       "11           0.925000           0.922917  ...         0.924687   \n",
       "\n",
       "    std_test_score  rank_test_score  split0_train_score  split1_train_score  \\\n",
       "0         0.011794               12            0.999609            0.999740   \n",
       "1         0.004172                4            0.975000            0.975651   \n",
       "2         0.003080                8            0.920312            0.916797   \n",
       "3         0.010160                9            1.000000            1.000000   \n",
       "4         0.005002                1            0.998958            0.999349   \n",
       "5         0.006245                6            0.960677            0.960547   \n",
       "6         0.010160                9            1.000000            1.000000   \n",
       "7         0.004857                2            1.000000            1.000000   \n",
       "8         0.002150                5            0.994010            0.994792   \n",
       "9         0.010160                9            1.000000            1.000000   \n",
       "10        0.004857                2            1.000000            1.000000   \n",
       "11        0.002668                7            1.000000            1.000000   \n",
       "\n",
       "    split2_train_score  split3_train_score  split4_train_score  \\\n",
       "0             0.999740            0.999740            0.999870   \n",
       "1             0.974740            0.975260            0.975391   \n",
       "2             0.921224            0.918880            0.919271   \n",
       "3             1.000000            1.000000            1.000000   \n",
       "4             0.998828            0.998958            0.999219   \n",
       "5             0.959375            0.962240            0.961458   \n",
       "6             1.000000            1.000000            1.000000   \n",
       "7             1.000000            1.000000            1.000000   \n",
       "8             0.993490            0.994141            0.992839   \n",
       "9             1.000000            1.000000            1.000000   \n",
       "10            1.000000            1.000000            1.000000   \n",
       "11            1.000000            1.000000            1.000000   \n",
       "\n",
       "    mean_train_score  std_train_score  \n",
       "0           0.999740         0.000082  \n",
       "1           0.975208         0.000315  \n",
       "2           0.919297         0.001495  \n",
       "3           1.000000         0.000000  \n",
       "4           0.999062         0.000191  \n",
       "5           0.960859         0.000959  \n",
       "6           1.000000         0.000000  \n",
       "7           1.000000         0.000000  \n",
       "8           0.993854         0.000656  \n",
       "9           1.000000         0.000000  \n",
       "10          1.000000         0.000000  \n",
       "11          1.000000         0.000000  \n",
       "\n",
       "[12 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning Results.\n",
    "cv_results = pd.DataFrame(model_cv.cv_results_)\n",
    "cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv_results['param_C'] = cv_results['param_C'].astype('int')\n",
    "#Returning plots.\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "#Returning subplot 1/3.\n",
    "plt.subplot(131)\n",
    "gamma_01 = cv_results[cv_results['param_gamma']==0.01]\n",
    "plt.plot(gamma_01[\"param_C\"], gamma_01[\"mean_test_score\"])\n",
    "plt.plot(gamma_01[\"param_C\"], gamma_01[\"mean_train_score\"])\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title(\"Gamma=0.01\")\n",
    "plt.ylim([0.60, 1])\n",
    "plt.legend(['test accuracy', 'train accuracy'], loc='upper left')\n",
    "plt.xscale('log') \n",
    "\n",
    "#Returning subplot 2/3.\n",
    "plt.subplot(132)\n",
    "gamma_001 = cv_results[cv_results['param_gamma']==0.001]\n",
    "plt.plot(gamma_001[\"param_C\"], gamma_001[\"mean_test_score\"])\n",
    "plt.plot(gamma_001[\"param_C\"], gamma_001[\"mean_train_score\"])\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title(\"Gamma=0.001\")\n",
    "plt.ylim([0.60, 1])\n",
    "plt.legend(['test accuracy', 'train accuracy'], loc='upper left')\n",
    "plt.xscale('log')\n",
    "\n",
    "#Returning subplot 3/3.\n",
    "plt.subplot(133)\n",
    "gamma_0001 = cv_results[cv_results['param_gamma']==0.0001]\n",
    "plt.plot(gamma_0001[\"param_C\"], gamma_0001[\"mean_test_score\"])\n",
    "plt.plot(gamma_0001[\"param_C\"], gamma_0001[\"mean_train_score\"])\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title(\"Gamma=0.0001\")\n",
    "plt.ylim([0.60, 1])\n",
    "plt.legend(['test accuracy', 'train accuracy'], loc='upper left')\n",
    "plt.xscale('log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "#Specifying 'best_score'.\n",
    "best_score = model_cv.best_score_\n",
    "best_hyperparams = model_cv.best_params_\n",
    "#Returning 'best_hyperparams'.\n",
    "print(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.9483333333333334 \n",
      "\n",
      "[[199   0   1   0   1   1   2   0   3   0]\n",
      " [  0 259   2   0   2   0   0   0   0   0]\n",
      " [  0   3 242   4   2   0   0   0   3   0]\n",
      " [  0   0   3 236   0   2   0   2   3   2]\n",
      " [  0   0   3   1 215   0   1   1   0   3]\n",
      " [  1   2   3   6   1 223   3   0   1   0]\n",
      " [  3   0   6   0   1   3 244   0   0   0]\n",
      " [  0   1   2   2   4   0   0 251   1   3]\n",
      " [  1   1   3   3   3   1   2   0 207   2]\n",
      " [  1   2   5   1   7   1   0   2   1 200]] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=10, gamma=0.001, kernel=\"rbf\")\n",
    "\n",
    "#Fitting the model.\n",
    "model.fit(x_train, y_train)\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "#Returning 'accuracy'.\n",
    "print(\"accuracy\", metrics.accuracy_score(y_test, y_pred), \"\\n\")\n",
    "print(metrics.confusion_matrix(y_test, y_pred), \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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