Athira Prakash Assignment Model Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab4e075e",
   "metadata": {},
   "source": [
    "## <font color='Brown' align='center'> Assignment – Model Selection</font>\n",
    "Submitted By:-\n",
    "### Athira Prakash \n",
    "#### DSA March 2023 B1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Do the following on titanic dataset \n",
    "1. Load the dataset into python environment \n",
    "2. Do all the necessary pre-processing steps \n",
    "3. Create kNN and SVM models \n",
    "4. Do k-fold and stratified stratified k-fold cross validation techniques and find the \n",
    "average accuracy score of the models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0269ab",
   "metadata": {},
   "source": [
    "### Load the dataset into python environment|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd90ddf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.simplefilter(action = 'ignore', category = FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "598ca874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data=pd.read_csv(r\"D:\\DSA\\data\\titanic_dataset .csv\")\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac0634da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a4ecea",
   "metadata": {},
   "source": [
    "### Removing unnecessory columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45307fc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['PassengerId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33ae495b",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('PassengerId',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be333934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3e2a972",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('Name',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80d36a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('Ticket',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed97d8cd",
   "metadata": {},
   "source": [
    "### Missing Value Handle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc0e7586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8fd8396e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 77.1 % missing values in the column Cabin\n"
     ]
    }
   ],
   "source": [
    "print('There are', np.round(titanic_data['Cabin'].isna().mean()*100,2), '% missing values in the column Cabin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0bce141",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "233f9935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(titanic_data['Age'])\n",
    "plt.title('Distribution of Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8df223fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Age'].fillna(titanic_data['Age'].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e844c015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ec2ce87",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2082cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb305264",
   "metadata": {},
   "source": [
    "### Outlier Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "659e9dbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhYAAAGxCAYAAAA+tv8YAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgu0lEQVR4nO3de5AV1Z0H8N8F4iDIXAIBkXh1CG4i4lvUKCbBjS+kLGMqJhpEombLZEmiUnn4SoxGM2GtdU2tG6Oui+sa1NRWMG6yJmoiPtYH4spGDSoYELJKIKzMBdRrHHr/6JqBkQHmzpyZ4Q6fT1XX0N2nT/+4VNHfOX26byHLsiwAABLo19sFAAB9h2ABACQjWAAAyQgWAEAyggUAkIxgAQAkI1gAAMkIFgBAMoIFAJCMYAFdddttEYVC22XEiIhJkyJ+8Yverm6ThoaIL3yh+uPefDPiu9+NmDcvbT0REcuWRUyZEjFsWP65XXjh1ttu2BAxa1bEQQdF1NdHDBkSMXZsxGc/G/Hww5vazZuX97V5vV/4QsRuu3Wspo6eB2jXgN4uAPqM2bMj9t03IssiVq6MuOGGiFNOibj33vxnrXrzzYgrr8z/PGlS2r4vuijiqaci/uVfIkaNithjj/bbNTdHnHBCxHPPRXzjGxFHHJFvX7w44j/+I+LRRyM+8Yl826GHRjzxRMR++1VfTzXnAdolWEAq++8fMWHCpvWTTop4//sj7ryztoNFd3r++fzi/alPbbvdI49EPP54HkDOOWfT9hNPjPjKVyI2bty0rb4+4qMf7Vw91ZwHaJdbIdBdBg6M2GWXiPe9r+32//u/iL/924gPfjDf/6EPRVx2WUSlku9/++2IQw6J2GefiKamTcetXJn/Vj9pUv6bdcSmIf4XXoj45CcjBg/Ob8N85Sv5SMP2LF8ecdZZESNHRtTVRYwbF/H3f7/pArpsWd5fRD5q0XKrZ3u3VLbXb8vtiiVLIu67b1O/y5a139+aNfnPrY1o9Nvsv7L2boW02N7nVM15Wm6BPfBAHkKGDcv7PeWUiD/8of3jYScgWEAqzc0R774b8Ze/RPzxj/l8gQ0bIj7/+U1t3n474thjI26/PWLmzIhf/jK/AP/d30V8+tN5m4EDI37604hVqyLOPTfftnFjxNSp+W2WO++M6N9/U59/+UvEySfnF8x77skvljfdFPG5z2273tWrI44+OuL++yO+9738ls1xx0V8/et5HxH5BfZXv8r/fN55+S2GJ56I+Pa3u9Zvy+2KUaMiJk7c1O/WLugTJuQB7YILIn7yk4jXX9/23609HfmcOnOe887LA8ecORHXXx8xf34e/taurb5G6AsyoGtmz86y/JLfdqmry7If/aht2x//ON/305+23T5rVr79/vs3bbv77nzb9ddn2Xe+k2X9+rXdn2VZNn163uaHP2y7/Zpr8u2PPbZp29575+1bXHxx3uapp9oe++UvZ1mhkGUvvZSvr16dt7viig59HB3ut6WmKVM61u+tt2bZbrtt+nz32CPLzj47yx55pG27hx7K9z/00KZt1XxOHT1Py7/7aae13f5f/5Vvv/rqjv29oI8xYgGp3H57xNNP58t990VMnx4xY0Y+ibPFb3+bD5d/5jNtj225tfCb32za9tnPRnz5y/kkwquvjrj00ojjj2//3FOntl1vGSV56KGt1/vb3+YTHFsmKG5eS5bl+zuju/o999x8JGjOnIivfS2iVIq44458MuW113asj458TtWe5719Hn10xN57b/uzhz5MsIBUxo3Lh9InTMgnbt50U/6EwTe/uWlYfM2afPi/UGh77MiREQMGbLrH3+Lcc/Mh/AED8otcewYMiBg+vO22UaM2nW9r1qxp/9bD6NHbP3ZbuqvfiIhiMeLMMyN++MP8aZLf/S5i993zOSrbu/VQzedUzXla+njvtq78PaGGCRbQnQ48MOKttyJefjlfHz484k9/yn9z39yqVfn8jA98YNO2DRsipk2L+PCHI3bdNeKLX2z/HO++u+VFbOXKTefbmuHD259D8Npr+c/Na6lGd/XbnvHjI844Iw9fLZ/x1nT2c9reeVr6eO+27fUJfZRgAd1p4cL8Z8uTFZ/8ZMT69fnkwc3dfvum/S2+9KX86Yqf/Szi1lvzSZD/8A/tn+cnP2m7PmdO/nNb75345Ccjfv/7iP/+7y1rKRTySaYR+VMdEXlA6oiO9luNNWsi3nmn/X0vvpj/bBkR2ZbtfU6dOc97+3z88YhXX03/zg+oEd5jAak8/3z+W3FEfoH62c/yRxFPOy1izJh8+9lnR/zTP+XzL5YtizjggIjHHov4/vfzJxaOOy5v98//nN/Xnz07/215/Pj8KYZvfSt/imLz+Qu77JI/yrl+fcThh+cXtquvjpg8OeKYY7Ze70UX5Rf7KVMirroqnxfwy19G/OhH+dyOD384bzdkSL7v5z/PQ8OwYfmoQ0ND1/qtxkMP5U9qTJ2az2EYPjwf5bnzzvyplbPPjthzz2330ZHPqTPnWbAgH006/fSIFSvy2yUf/GD+SDHsjHp79ijUvPaeCikWs+zgg7Psuuuy7O2327ZfsybLvvSl/GmDAQPyJyMuuWRTu9/9Lst23bXtExxZlu8/7LAsa2jIsjfeyLdNn55lgwfnx0yalB83bFj+BMb69W2Pf+9TIVmWZa++mmWf/3yWDR+eZe97X5Z95CNZdu21Wdbc3Lbdgw9m2SGH5E+6RGzZz3t1tN+OPhWyYkWWXX55lk2cmGWjRuWf25AhWXbkkVn2j/+YZe++u6nt1p4K6cjnVM15Wv7d778/y6ZNy7KhQ/N+Tz45yxYv3v7fCfqoQpa992YvUDO+8IWIf//3/LdwetZtt+Uvxnr66bZvXIWdnDkWAEAyggUAkIxbIQBAMkYsAIBkBAsAIBnBAgBIpsdfkLVx48Z47bXXYsiQIVF47/clAAA7pCzLYt26dTF69Ojo12/r4xI9Hixee+21KJVKPX1aACCBFStWxJ7beNNtjweLIUOGREReWH19fU+fHgDohHK5HKVSqfU6vjU9Hixabn/U19cLFgBQY7Y3jcHkTQAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLIInVq1fHmDFjYrfddosxY8bE6tWre7skoBf0+Js3gb5n6NCh0dTU1Lq+YcOGGDlyZBSLxVi7dm3vFQb0uKpGLN599924/PLLY8yYMbHrrrvGhz70objqqqti48aN3VUfsIPbPFSMHz8+fvGLX8T48eMjIqKpqSmGDh3ai9UBPa2qEYtZs2bFj3/84/jXf/3XGD9+fCxYsCDOOeecKBaLccEFF3RXjcAOavXq1a2hoqmpqfX7f6ZMmRLlcjmKxWI0NTXF6tWrY8SIEb1ZKtBDqgoWTzzxRJx66qkxZcqUiIhoaGiIO++8MxYsWLDVYyqVSlQqldb1crncyVKBHc0RRxwREflIxXu/VLC+vj7GjRsXixYtiiOOOCKWLl3aGyUCPayqWyHHHHNM/OY3v4mXX345IiL+53/+Jx577LE4+eSTt3pMY2NjFIvF1qVUKnWtYmCH0TJBc9asWe3uv+aaa9q0A/q+QpZlWUcbZ1kWl156acyaNSv69+8fzc3Ncc0118Qll1yy1WPaG7EolUpthk2B2jRmzJhYtmxZjB8/Pp5//vkt9u+3336xaNGiaGhoMGIBNW7z25vbun5XdSvk7rvvjjvuuCPmzJkT48ePj4ULF8aFF14Yo0ePjunTp7d7TF1dXdTV1VVXPVAT5s+fHyNHjowXXnghyuVym/9syuVyLFq0qLUdsHOo6lbIN77xjbj44ovjjDPOiAMOOCCmTZsWF110UTQ2NnZXfcAObMSIEVEsFiMiolgsxn777Rdz586N/fbbr812Ezdh51HViMWbb74Z/fq1zSL9+/f3uCnsxNauXdv6yOmiRYvi05/+dOs+77GAnU9VIxannHJKXHPNNfHLX/4yli1bFnPnzo3rrrsuTjvttO6qD6gBa9eujVWrVkVDQ0MMHjw4GhoaYtWqVUIF7ISqmry5bt26+Pa3vx1z586NVatWxejRo+PMM8+M73znO7HLLrt0qI+OTv4AAHYcHb1+VxUsUhAsAKD2dPT67UvIAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASKaqYNHQ0BCFQmGLZcaMGd1VHwBQQwZU0/jpp5+O5ubm1vXnn38+jj/++Dj99NOTFwYA1J6qgsWIESParP/gBz+IsWPHxic+8YmtHlOpVKJSqbSul8vlKksEAGpFp+dYvPPOO3HHHXfEueeeG4VCYavtGhsbo1gsti6lUqmzpwQAdnCFLMuyzhz405/+ND7/+c/H8uXLY/To0Vtt196IRalUiqampqivr+/MqQGAHlYul6NYLG73+l3VrZDN3XrrrTF58uRthoqIiLq6uqirq+vsaQCAGtKpYPHqq6/Ggw8+GD/72c9S1wMA1LBOzbGYPXt2jBw5MqZMmZK6HgCghlUdLDZu3BizZ8+O6dOnx4ABnb6TAgD0QVUHiwcffDCWL18e5557bnfUAwDUsKqHHE444YTo5IMkAEAf57tCAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASKbqYPG///u/cdZZZ8Xw4cNj0KBBcfDBB8czzzzTHbUBADVmQDWN33jjjZg4cWIce+yxcd9998XIkSPjlVdeiaFDh3ZTeQBALakqWMyaNStKpVLMnj27dVtDQ8M2j6lUKlGpVFrXy+VydRUCADWjqlsh9957b0yYMCFOP/30GDlyZBxyyCFxyy23bPOYxsbGKBaLrUupVOpSwQDAjquQZVnW0cYDBw6MiIiZM2fG6aefHvPnz48LL7wwbrrppjj77LPbPaa9EYtSqRRNTU1RX1/fxfIBgJ5QLpejWCxu9/pdVbDYZZddYsKECfH444+3bvva174WTz/9dDzxxBNJCwMAdhwdvX5XdStkjz32iP3226/NtnHjxsXy5cs7VyUA0KdUFSwmTpwYL730UpttL7/8cuy9995JiwIAalNVweKiiy6KJ598Mr7//e/HkiVLYs6cOXHzzTfHjBkzuqs+AKCGVBUsDj/88Jg7d27ceeedsf/++8f3vve9uP7662Pq1KndVR8AUEOqmryZgsmbAFB7umXyJgDAtggWAEAyggUAkIxgAQAkI1gAAMkIFgBAMoIFAJCMYAEAJCNYAADJCBYAQDKCBQCQjGABACQjWAAAyQgWAEAyggUAkIxgAQAkI1gAAMkIFgBAMoIFAJCMYAEAJCNYAADJCBYAQDKCBQCQjGABACQjWAAAyQgWAEAyggUAkIxgAQAkI1gAAMkIFkASTU1Nccwxx8Ree+0VxxxzTDQ1NfV2SUAvGNDbBQC1b5999olXXnmldX3FihUxdOjQGDt2bCxZsqQXKwN6WlUjFt/97nejUCi0WUaNGtVdtQE1YPNQcdJJJ8UTTzwRJ510UkREvPLKK7HPPvv0ZnlAD6t6xGL8+PHx4IMPtq73798/aUFA7WhqamoNFRs2bIhBgwZFRMR9990Xb775ZgwePDheeeWVaGpqimKx2JulAj2k6jkWAwYMiFGjRrUuI0aM2Gb7SqUS5XK5zQL0DVOmTImIfKSiJVS0GDRoUJxwwglt2gF9X9XBYvHixTF69OgYM2ZMnHHGGfGHP/xhm+0bGxujWCy2LqVSqdPFAjuW5cuXR0TEFVdc0e7+yy+/vE07oO+rKlgceeSRcfvtt8evf/3ruOWWW2LlypVx9NFHx5o1a7Z6zCWXXBJNTU2ty4oVK7pcNLBj2GuvvSIi4sorr2x3/9VXX92mHdD3FbIsyzp78IYNG2Ls2LHxzW9+M2bOnNmhY8rlchSLxWhqaor6+vrOnhrYATQ1NcXQoUMjou0ci4honWMREbF27VpzLKDGdfT63aX3WAwePDgOOOCAWLx4cVe6AWpUsViMsWPHRkT+/8GJJ54Yjz76aJx44omtoWLs2LFCBexEuhQsKpVKLFq0KPbYY49U9QA1ZsmSJa3h4v7774+Pf/zjcf/990dEeI8F7ISqChZf//rX4+GHH46lS5fGU089FZ/5zGeiXC7H9OnTu6s+oAYsWbIk1q5dGxMnToxSqRQTJ06MtWvXChWwE6rqPRZ//OMf48wzz4w///nPMWLEiPjoRz8aTz75ZOy9997dVR9QI4rFYjz22GO9XQbQy6oKFnfddVd31QEA9AG+hAwASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsACSmD9/fhQKhdZl/vz5vV0S0AsG9HYBQO0rFApbbDvyyCMjIiLLsp4uB+hFXRqxaGxsjEKhEBdeeGGicoBa895Q8Td/8zfb3A/0bZ0OFk8//XTcfPPNceCBB6asB6ghm9/uWLx4cWRZFjfffHNkWRaLFy9utx3Qt3UqWKxfvz6mTp0at9xyS7z//e/fZttKpRLlcrnNAvQNLbc7IiL22WefNvs2X9+8HdC3dSpYzJgxI6ZMmRLHHXfcdts2NjZGsVhsXUqlUmdOCezA3nv7o8W0adN6uBKgtxWyKmdW3XXXXXH11VfHggULYuDAgTFp0qQ4+OCD4/rrr2+3faVSiUql0rpeLpejVCpFU1NT1NfXd6l4oHdtPn+ivf9KtrcfqB3lcjmKxeJ2r99VjVisWLEiLrjggvjJT34SAwcO7NAxdXV1UV9f32YB+oannnqq9c9Llixps2/z9c3bAX1bVSMW99xzT5x22mnRv3//1m3Nzc1RKBSiX79+UalU2uxrT0cTD1Ab3vvUx7Rp0+Lf/u3f2mwzWgG1r6PX76qCxbp16+LVV19ts+2cc86JfffdN771rW/F/vvvn6wwoHZs65FSoQL6ho5ev6t6QdaQIUO2CA+DBw+O4cOHdyhUAH1TlmUxf/78Nk9/PPXUU3HEEUf0YlVAb/DmTSCJI444wugE0PVgMW/evARlAAB9gS8hAwCSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsgCQWLlwYhUKhdVm4cGFvlwT0ggG9XQBQ+wqFwhbbDjnkkIiIyLKsp8sBelFVIxY33nhjHHjggVFfXx/19fVx1FFHxX333dddtQE1YPNQ0a9fv7jooouiX79+7e4H+r6qgsWee+4ZP/jBD2LBggWxYMGC+Ou//us49dRT44UXXuiu+oAd2Oa3O5YuXRrNzc1x3XXXRXNzcyxdurTddkDfVsi6OE45bNiwuPbaa+O8885rd3+lUolKpdK6Xi6Xo1QqRVNTU9TX13fl1EAvaxmN6NevXzQ3N2+xv3///rFx48aIcEsEal25XI5isbjd63enJ282NzfHXXfdFRs2bIijjjpqq+0aGxujWCy2LqVSqbOnBHZQF1xwQbvbzz///B6uBOhtVY9YPPfcc3HUUUfF22+/HbvttlvMmTMnTj755K22N2IBfZcRC9h5dNuIxUc+8pFYuHBhPPnkk/HlL385pk+fHr///e+32r6urq51smfLAvQNzz77bEREbNy4MZYtW9Zm37Jly1pDRUs7oO/r8hyL4447LsaOHRs33XRTh9p3NPEAteG9T4Wcf/75cdNNN7WGigijFdAXdPT63eX3WGRZ1uZWB7BzybKsNVxs3Lgxbrzxxi32AzuPqm6FXHrppfHoo4/GsmXL4rnnnovLLrss5s2bF1OnTu2u+oAakGXZFrc7nn32WaECdkJVjVj86U9/imnTpsXrr78exWIxDjzwwPjVr34Vxx9/fHfVB9SIgw8+WJAAqgsWt956a3fVAQD0Ab6EDABIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGSqChaNjY1x+OGHx5AhQ2LkyJHxqU99Kl566aXuqg0AqDFVBYuHH344ZsyYEU8++WQ88MAD8e6778YJJ5wQGzZs6K76AIAaUsiyLOvswatXr46RI0fGww8/HB//+MfbbVOpVKJSqbSul8vlKJVK0dTUFPX19Z09NZDIm2++GS+++GKSvt56661YtmxZNDQ0xK677trl/vbdd98YNGhQgsqAriqXy1EsFrd7/R7QlZM0NTVFRMSwYcO22qaxsTGuvPLKrpwG6EYvvvhiHHbYYb1dRrueeeaZOPTQQ3u7DKAKnR6xyLIsTj311HjjjTfi0Ucf3Wo7IxawY0s5YrFo0aI466yz4o477ohx48Z1uT8jFrDj6PYRi6985Svxu9/9Lh577LFttqurq4u6urrOngboZoMGDUo+KjBu3DgjDbCT6lSw+OpXvxr33ntvPPLII7HnnnumrgkAqFFVBYssy+KrX/1qzJ07N+bNmxdjxozprroAgBpUVbCYMWNGzJkzJ37+85/HkCFDYuXKlRERUSwWk8wABwBqW1XvsbjxxhujqakpJk2aFHvssUfrcvfdd3dXfQBADan6VggAwNb4rhAAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSqTpYPPLII3HKKafE6NGjo1AoxD333NMNZQEAtajqYLFhw4Y46KCD4oYbbuiOegCAGjag2gMmT54ckydP7nD7SqUSlUqldb1cLld7SqAdixcvjnXr1vV2GW0sWrSozc8dxZAhQ+Kv/uqversM2ClUHSyq1djYGFdeeWV3nwZ2KosXL44Pf/jDvV3GVp111lm9XcIWXn75ZeECekC3B4tLLrkkZs6c2bpeLpejVCp192mhT2sZqbjjjjti3LhxvVzNJm+99VYsW7YsGhoaYtddd+3tciIiHz0566yzdrjRHeiruj1Y1NXVRV1dXXefBnZK48aNi0MPPbS3y2hj4sSJvV0C0Is8bgoAJCNYAADJVH0rZP369bFkyZLW9aVLl8bChQtj2LBhsddeeyUtDgCoLVUHiwULFsSxxx7but4yMXP69Olx2223JSsMAKg9VQeLSZMmRZZl3VELAFDjzLEAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgGcECAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGQECwAgmU4Fix/96EcxZsyYGDhwYBx22GHx6KOPpq4LAKhBVQeLu+++Oy688MK47LLL4tlnn42PfexjMXny5Fi+fHl31AcA1JAB1R5w3XXXxXnnnRdf/OIXIyLi+uuvj1//+tdx4403RmNj4xbtK5VKVCqV1vVyudyFcoGIiLfX/V8cMqpfvPrkvbHr2pe71FelUonXXnstUWVpjR49Ourq6rrUx8qlS+OQUf2i8O7biaoCtqWqYPHOO+/EM888ExdffHGb7SeccEI8/vjj7R7T2NgYV155ZecrBLbwpxcei/8+f7eIVf8Qsarr/R3c9S66x4qudzEuIk4+f7dYnq3pemfAdlUVLP785z9Hc3Nz7L777m2277777rFy5cp2j7nkkkti5syZrevlcjlKpVInSgVafOy082Lu3IiGhoYYOHBgl/rq6yMWERGDBw+OvQ75ZIKKgO2p+lZIREShUGiznmXZFtta1NXVJfmPAdjkA3uU4rS//W6y/g5O1hOws6tq8uYHPvCB6N+//xajE6tWrdpiFAMA2PlUFSx22WWXOOyww+KBBx5os/2BBx6Io48+OmlhAEDtqfpWyMyZM2PatGkxYcKEOOqoo+Lmm2+O5cuXx5e+9KXuqA8AqCFVB4vPfe5zsWbNmrjqqqvi9ddfj/333z/+8z//M/bee+/uqA8AqCGFLMuynjxhuVyOYrEYTU1NUV9f35OnBgA6qaPXb98VAgAkI1gAAMkIFgBAMoIFAJCMYAEAJCNYAADJCBYAQDKCBQCQTKe+3bQrWt7HVS6Xe/rUAEAntVy3t/dezR4PFuvWrYuIiFKp1NOnBgC6aN26dVEsFre6v8df6b1x48Z47bXXYsiQIVEoFHry1EA3K5fLUSqVYsWKFV7ZD31MlmWxbt26GD16dPTrt/WZFD0eLIC+y3cBASZvAgDJCBYAQDKCBZBMXV1dXHHFFVFXV9fbpQC9xBwLACAZIxYAQDKCBQCQjGABACQjWAAAyQgWAEAyggXQZY888kiccsopMXr06CgUCnHPPff0dklALxEsgC7bsGFDHHTQQXHDDTf0dilAL+vxbzcF+p7JkyfH5MmTe7sMYAdgxAIASEawAACSESwAgGQECwAgGcECAEjGUyFAl61fvz6WLFnSur506dJYuHBhDBs2LPbaa69erAzoab42HeiyefPmxbHHHrvF9unTp8dtt93W8wUBvUawAACSMccCAEhGsAAAkhEsAIBkBAsAIBnBAgBIRrAAAJIRLACAZAQLACAZwQIASEawAACSESwAgGT+H6lLjFX43aMuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_col = titanic_data.select_dtypes(include=['float', 'int'])\n",
    "for col in num_col:\n",
    "    plt.figure()\n",
    "    plt.boxplot(titanic_data[col])\n",
    "    plt.title('Boxplot of {}'.format(col), color = 'r')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "762b833e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = titanic_data['SibSp'].quantile(0.25)\n",
    "Q3 = titanic_data['SibSp'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "low = Q1 - (1.5 * IQR)\n",
    "upp = Q3 + (1.5 * IQR)\n",
    "out_ind = titanic_data[(titanic_data['SibSp'] > upp) | (titanic_data['Fare'] < low)].index\n",
    "titanic_data.drop(out_ind, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9fa57eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['Age', 'Fare','Parch']:\n",
    "    Q1 = titanic_data[col].quantile(0.25)\n",
    "    Q3 =titanic_data[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    low = Q1 - (1.5 * IQR)\n",
    "    upp = Q3 + (1.5 * IQR)\n",
    "    titanic_data[col] = np.where(titanic_data[col] > upp, upp, np.where(titanic_data[col] < low, low, titanic_data[col]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40c30a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiwAAAGxCAYAAABBZ+3pAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAqjklEQVR4nO3de3BUZZ7/8U9z6zAzpDUguQwhoIuEwIpJc8llYKSAYFBKZlWyO2MAB8dlFlcg5f6ceBvRWVPsjG6IXJTaaCrFEDKz4eYKK2EVAksvO4YEd3bBgVqcZGP3MrDaTTISBM7vj1NpaHLtEMiT8H5VdcXz9Pc8/T1tlf3xOed0OyzLsgQAAGCwfj3dAAAAQEcILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsgKmKiyWHI/Rxxx3SffdJ//RPPd3dFaNGSYsXh7/fH/8ovfyytG9f9/YjSZ99Jj3wgBQVZb9vK1a0XdvYKK1eLU2cKEVGSkOGSHfdJS1YIO3ff6Vu3z57rqv7XbxY+ta3OtdTZ18HQKsG9HQDADrw7rtSYqJkWZLPJ61dK82bJ+3caf/trf74R2nVKvuf77uve+deuVI6fFh65x0pJkaKjW297tIlKTNT+o//kP7mb6QpU+zxEyek996TDhyQvvtdeywlRfJ4pKSk8PsJ53UAtIrAAphuwgRp0qQr2/ffL91+u1Ra2rsDy43029/aoWD+/PbrKiulQ4fsYPP441fG58yRnnpKunz5ylhkpJSa2rV+wnkdAK3ilBDQ20RESIMGSQMHho7/3/9Jf/VX0re/bT9/553S889LTU328+fPS8nJ0p/8ieT3X9nP57NXIe67z14JkK6c6vjP/5RmzpS++U37dNRTT9krIx2prZUee0waPlxyOqVx46TXX7/ywfzZZ/Z8kr3K0nzKq6NTSx3N23za5uRJaffuK/N+9lnr8509a/9tawWm31X/iWztlFCzjt6ncF6n+VRgRYUdbqKi7HnnzZP++79b3x+4BRBYANNduiRdvCh9/bX0P/9jX4/R2Ch9//tXas6fl2bMkEpKpNxc6f337Q/2v/s76c/+zK6JiJB+9Svp9Gnphz+0xy5fln7wA/t0U2mp1L//lTm//lqaO9f+IN6+3f4QfvttKTu7/X7/8AcpPV3as0d69VX71NWsWdIzz9hzSPYH9z//s/3PS5bYp1o8HunFF69v3ubTNjExUkbGlXnbCgqTJtnBb/ly6Ze/lLze9o+tNZ15n7ryOkuW2EFm82apoED693+3Q+WXX4bfI9AXWADM9O67lmVHidCH02lZ69eH1r71lv3cr34VOr56tT2+Z8+VsbIye6ygwLJeesmy+vULfd6yLGvRIrtmzZrQ8b/9W3v84MErYwkJdn2zn/zErjl8OHTfH//YshwOy/r0U3v7D3+w63760069HZ2et7mnBx7o3LxFRZb1rW9deX9jYy1r4ULLqqwMrfvoI/v5jz66MhbO+9TZ12n+9/6974WO/+u/2uM/+1nnjgvoY1hhAUxXUiL95jf2Y/duadEiadky++LbZh9+aJ82eOSR0H2bT7H8y79cGVuwQPrxj+2LP3/2M+m556TZs1t/7R/8IHS7eVXno4/a7vfDD+0LU5svLL26F8uyn++KGzXvD39or1xt3iw9/bQUHy9t2mRfBPvzn3dujs68T+G+zrVzpqdLCQntv/dAH0ZgAUw3bpx9SmHSJPuC27fftu84+X//78rpgbNn7dMgDkfovsOHSwMGXLmGotkPf2ifyhgwwP7wbM2AAdLQoaFjMTFXXq8tZ8+2fgomLq7jfdtzo+aVJJdL+ou/kNasse8u+uQTKTravgaoo1Mw4bxP4bxO8xzXjl3PcQK9GIEF6I3uuUf66ivpd7+zt4cOlf73f+2VhqudPm1f/zJs2JWxxkYpJ0e6+25p8GDpiSdaf42LF1t+OPp8V16vLUOHtn6Nxuef23+v7iUcN2re1owfL/35n9uhrvk9bktX36eOXqd5jmvHOpoT6KMILEBvVFNj/22+02bmTKmhwb7o82olJVeeb7Z0qX23zdatUlGRffHq3/9966/zy1+Gbm/ebP9t73tTZs6U/uu/pCNHWvbicNgXB0v2XT6SHbw6o7PzhuPsWenChdafO37c/tu8gtOejt6nrrzOtXMeOiT9/vfd/501QC/B97AApvvtb+3/i5fsD76tW+1bXr/3PWn0aHt84UJp3Tr7+pbPPpP+9E+lgwel116z72CZNcuu+4d/sK+bePdd+//ux4+372p59ln7rpqrrw8ZNMi+ZbihQZo82f7A/NnPpKws6TvfabvflSvtEPHAA9Irr9jXXbz/vrR+vX3tzN1323VDhtjP7dhhh5GoKHuVZNSo65s3HB99ZN+584Mf2NeIDB1qr0qVltp3MS1cKI0Y0f4cnXmfuvI6H39sr349+qhUV2efNvr2t+1b14FbUU9f9QugDa3dJeRyWda991rWG29Y1vnzofVnz1rW0qX23ScDBth3yuTlXan75BPLGjw49I4ey7Kfd7sta9Qoy/riC3ts0SLL+uY37X3uu8/eLyrKviOnoSF0/2vvErIsy/r97y3r+9+3rKFDLWvgQMsaO9ayfv5zy7p0KbRu717LSk6273ySWs5zrc7O29m7hOrqLOuFFywrI8OyYmLs923IEMuaOtWy3nzTsi5evFLb1l1CnXmfwnmd5n/ve/ZYVk6OZd12mz3v3LmWdeJEx8cE9FEOy7r2pDeAW97ixdI//qO9aoCbq7jY/sK43/wm9BuOgVsc17AAAADjEVgAAIDxOCUEAACMxwoLAAAwHoEFAAAYj8ACAACM12e+OO7y5cv6/PPPNWTIEDmu/T0VAABgJMuydO7cOcXFxalfv7bXUfpMYPn8888VHx/f020AAIAuqKur04h2vlm6zwSWIUOGSLIPODIysoe7AQAAnREIBBQfHx/8HG9LnwkszaeBIiMjCSwAAPQyHV3OwUW3AADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8sAJLfn6+Jk+erCFDhmj48OGaP3++Pv300w73279/v9xutyIiInTnnXfqrbfealFTXl6upKQkOZ1OJSUladu2beG0BgAA+rCwAsv+/fu1bNky/du//ZsqKip08eJFZWZmqrGxsc19Tp06pblz52ratGmqrq7Wc889p6efflrl5eXBGo/Ho+zsbOXk5Ojo0aPKycnRggULdPjw4a4fGQAA6DMclmVZXd35D3/4g4YPH679+/dr+vTprdY8++yz2rlzp44dOxYcW7p0qY4ePSqPxyNJys7OViAQ0O7du4M1999/v26//XaVlpa2Om9TU5OampqC282/9uj3+/nxQ8AAf/zjH3X8+PFumeurr77SZ599plGjRmnw4MHXPV9iYqK+8Y1vdENnAK5XIBCQy+Xq8PP7un6t2e/3S5KioqLarPF4PMrMzAwZmzNnjoqKivT1119r4MCB8ng8WrlyZYuagoKCNufNz8/XqlWrut48gBvq+PHjcrvdPd1Gq6qqqpSSktLTbQAIQ5cDi2VZys3N1Xe+8x1NmDChzTqfz6fo6OiQsejoaF28eFFnzpxRbGxsmzU+n6/NefPy8pSbmxvcbl5hAWCGxMREVVVVdctcx44d02OPPaZNmzZp3Lhx1z1fYmJiN3QF4GbqcmB56qmn9Mknn+jgwYMd1jocjpDt5rNQV4+3VnPt2NWcTqecTmc4LQO4ib7xjW90+yrGuHHjWBkBblFdCix//dd/rZ07d6qyslIjRoxotzYmJqbFSsnp06c1YMAADR06tN2aa1ddAADArSmsu4Qsy9JTTz2lrVu36sMPP9To0aM73CctLU0VFRUhY3v27NGkSZM0cODAdmvS09PDaQ8AAPRRYQWWZcuWadOmTdq8ebOGDBkin88nn8+nr776KliTl5enhQsXBreXLl2q3//+98rNzdWxY8f0zjvvqKioSM8880ywZvny5dqzZ49Wr16t48ePa/Xq1dq7d69WrFhx/UcIAAB6vbACy4YNG+T3+3XfffcpNjY2+CgrKwvWeL1e1dbWBrdHjx6tXbt2ad++fbr33nv16quvqrCwUA8//HCwJj09XVu2bNG7776re+65R8XFxSorK9PUqVO74RABAEBvd13fw2KSzt7HDaD3OXLkiNxuN7cjA31QZz+/+S0hAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxwg4slZWVmjdvnuLi4uRwOLR9+/Z26xcvXiyHw9HiMX78+GBNcXFxqzXnz58P+4AAAEDfE3ZgaWxs1MSJE7V27dpO1a9Zs0Zerzf4qKurU1RUlB599NGQusjIyJA6r9eriIiIcNsDAAB90IBwd8jKylJWVlan610ul1wuV3B7+/bt+uKLL/T444+H1DkcDsXExHR63qamJjU1NQW3A4FAp/cFAAC9y02/hqWoqEizZs1SQkJCyHhDQ4MSEhI0YsQIPfjgg6qurm53nvz8/GAYcrlcio+Pv5FtAwCAHnRTA4vX69Xu3bv1xBNPhIwnJiaquLhYO3fuVGlpqSIiIpSRkaETJ060OVdeXp78fn/wUVdXd6PbBwAAPSTsU0LXo7i4WLfddpvmz58fMp6amqrU1NTgdkZGhlJSUvTmm2+qsLCw1bmcTqecTueNbBcAABjipq2wWJald955Rzk5ORo0aFC7tf369dPkyZPbXWEBAAC3jpsWWPbv36+TJ09qyZIlHdZalqWamhrFxsbehM4AAIDpwj4l1NDQoJMnTwa3T506pZqaGkVFRWnkyJHKy8tTfX29SkpKQvYrKirS1KlTNWHChBZzrlq1SqmpqRozZowCgYAKCwtVU1OjdevWdeGQAABAXxN2YPn44481Y8aM4HZubq4kadGiRSouLpbX61VtbW3IPn6/X+Xl5VqzZk2rc3755Zd68skn5fP55HK5lJycrMrKSk2ZMiXc9gAAQB/ksCzL6ukmukMgEJDL5ZLf71dkZGRPtwOgGx05ckRut1tVVVVKSUnp6XYAdKPOfn7zW0IAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOOFHVgqKys1b948xcXFyeFwaPv27e3W79u3Tw6Ho8Xj+PHjIXXl5eVKSkqS0+lUUlKStm3bFm5rAACgjwo7sDQ2NmrixIlau3ZtWPt9+umn8nq9wceYMWOCz3k8HmVnZysnJ0dHjx5VTk6OFixYoMOHD4fbHgAA6IMGhLtDVlaWsrKywn6h4cOH67bbbmv1uYKCAs2ePVt5eXmSpLy8PO3fv18FBQUqLS1tdZ+mpiY1NTUFtwOBQNg9AQCA3uGmXcOSnJys2NhYzZw5Ux999FHIcx6PR5mZmSFjc+bM0aFDh9qcLz8/Xy6XK/iIj4+/IX0DAICed8MDS2xsrDZu3Kjy8nJt3bpVY8eO1cyZM1VZWRms8fl8io6ODtkvOjpaPp+vzXnz8vLk9/uDj7q6uht2DAAAoGeFfUooXGPHjtXYsWOD22lpaaqrq9MvfvELTZ8+PTjucDhC9rMsq8XY1ZxOp5xOZ/c3DAAAjNMjtzWnpqbqxIkTwe2YmJgWqymnT59useoCAABuTT0SWKqrqxUbGxvcTktLU0VFRUjNnj17lJ6efrNbAwAABgr7lFBDQ4NOnjwZ3D516pRqamoUFRWlkSNHKi8vT/X19SopKZFk3wE0atQojR8/XhcuXNCmTZtUXl6u8vLy4BzLly/X9OnTtXr1aj300EPasWOH9u7dq4MHD3bDIQIAgN4u7MDy8ccfa8aMGcHt3NxcSdKiRYtUXFwsr9er2tra4PMXLlzQM888o/r6eg0ePFjjx4/X+++/r7lz5wZr0tPTtWXLFr3wwgt68cUXddddd6msrExTp069nmMDAAB9hMOyLKunm+gOgUBALpdLfr9fkZGRPd0OgG505MgRud1uVVVVKSUlpafbAdCNOvv5zW8JAQAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMF3Zgqays1Lx58xQXFyeHw6Ht27e3W79161bNnj1bd9xxhyIjI5WWlqYPPvggpKa4uFgOh6PF4/z58+G2BwAA+qCwA0tjY6MmTpyotWvXdqq+srJSs2fP1q5du1RVVaUZM2Zo3rx5qq6uDqmLjIyU1+sNeURERITbHgAA6IMGhLtDVlaWsrKyOl1fUFAQsv3aa69px44deu+995ScnBwcdzgciomJ6fS8TU1NampqCm4HAoFO7wsAAHqXm34Ny+XLl3Xu3DlFRUWFjDc0NCghIUEjRozQgw8+2GIF5lr5+flyuVzBR3x8/I1sGwAA9KCbHlhef/11NTY2asGCBcGxxMREFRcXa+fOnSotLVVERIQyMjJ04sSJNufJy8uT3+8PPurq6m5G+wAAoAeEfUroepSWlurll1/Wjh07NHz48OB4amqqUlNTg9sZGRlKSUnRm2++qcLCwlbncjqdcjqdN7xnAADQ825aYCkrK9OSJUv061//WrNmzWq3tl+/fpo8eXK7KywAAODWcVNOCZWWlmrx4sXavHmzHnjggQ7rLctSTU2NYmNjb0J3AADAdGGvsDQ0NOjkyZPB7VOnTqmmpkZRUVEaOXKk8vLyVF9fr5KSEkl2WFm4cKHWrFmj1NRU+Xw+SdLgwYPlcrkkSatWrVJqaqrGjBmjQCCgwsJC1dTUaN26dd1xjAAAoJcLe4Xl448/VnJycvCW5NzcXCUnJ+ull16SJHm9XtXW1gbr3377bV28eFHLli1TbGxs8LF8+fJgzZdffqknn3xS48aNU2Zmpurr61VZWakpU6Zc7/EBAIA+wGFZltXTTXSHQCAgl8slv9+vyMjInm4HQDc6cuSI3G63qqqqlJKS0tPtAOhGnf385reEAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGCzuwVFZWat68eYqLi5PD4dD27ds73Gf//v1yu92KiIjQnXfeqbfeeqtFTXl5uZKSkuR0OpWUlKRt27aF2xoAAOijwg4sjY2NmjhxotauXdup+lOnTmnu3LmaNm2aqqur9dxzz+npp59WeXl5sMbj8Sg7O1s5OTk6evSocnJytGDBAh0+fDjc9gAAQB/ksCzL6vLODoe2bdum+fPnt1nz7LPPaufOnTp27FhwbOnSpTp69Kg8Ho8kKTs7W4FAQLt37w7W3H///br99ttVWlra6rxNTU1qamoKbgcCAcXHx8vv9ysyMrKrhwRA0okTJ3Tu3LmebiPo2LFjeuyxx7Rp0yaNGzeup9sJGjJkiMaMGdPTbQC9WiAQkMvl6vDze8CNbsTj8SgzMzNkbM6cOSoqKtLXX3+tgQMHyuPxaOXKlS1qCgoK2pw3Pz9fq1atuhEtA7e0EydO6O677+7pNlr12GOP9XQLLfzud78jtAA3wQ0PLD6fT9HR0SFj0dHRunjxos6cOaPY2Ng2a3w+X5vz5uXlKTc3N7jdvMIC4Po0r6yYtJrx1Vdf6bPPPtOoUaM0ePDgnm5H0pVVH5NWooC+7IYHFsk+dXS15rNQV4+3VnPt2NWcTqecTmc3dgngauPGjVNKSkpPtxGUkZHR0y0A6EE3/LbmmJiYFislp0+f1oABAzR06NB2a65ddQEAALemGx5Y0tLSVFFRETK2Z88eTZo0SQMHDmy3Jj09/Ua3BwAAeoGwTwk1NDTo5MmTwe1Tp06ppqZGUVFRGjlypPLy8lRfX6+SkhJJ9h1Ba9euVW5urn70ox/J4/GoqKgo5O6f5cuXa/r06Vq9erUeeugh7dixQ3v37tXBgwe74RABAEBvF/YKy8cff6zk5GQlJydLknJzc5WcnKyXXnpJkuT1elVbWxusHz16tHbt2qV9+/bp3nvv1auvvqrCwkI9/PDDwZr09HRt2bJF7777ru655x4VFxerrKxMU6dOvd7jAwAAfUDYKyz33Xef2vvqluLi4hZj3/3ud3XkyJF2533kkUf0yCOPhNsOAAC4BfBbQgAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA43UpsKxfv16jR49WRESE3G63Dhw40Gbt4sWL5XA4WjzGjx8frCkuLm615vz5811pDwAA9DFhB5aysjKtWLFCzz//vKqrqzVt2jRlZWWptra21fo1a9bI6/UGH3V1dYqKitKjjz4aUhcZGRlS5/V6FRER0bWjAgAAfcqAcHd44403tGTJEj3xxBOSpIKCAn3wwQfasGGD8vPzW9S7XC65XK7g9vbt2/XFF1/o8ccfD6lzOByKiYnpdB9NTU1qamoKbgcCgXAPBQAA9BJhrbBcuHBBVVVVyszMDBnPzMzUoUOHOjVHUVGRZs2apYSEhJDxhoYGJSQkaMSIEXrwwQdVXV3d7jz5+fnBMORyuRQfHx/OoQAAgF4krMBy5swZXbp0SdHR0SHj0dHR8vl8He7v9Xq1e/fu4OpMs8TERBUXF2vnzp0qLS1VRESEMjIydOLEiTbnysvLk9/vDz7q6urCORQAANCLhH1KSLJP31zNsqwWY60pLi7Wbbfdpvnz54eMp6amKjU1NbidkZGhlJQUvfnmmyosLGx1LqfTKafTGX7zAACg1wlrhWXYsGHq379/i9WU06dPt1h1uZZlWXrnnXeUk5OjQYMGtd9Uv36aPHlyuyssAADg1hFWYBk0aJDcbrcqKipCxisqKpSent7uvvv379fJkye1ZMmSDl/HsizV1NQoNjY2nPYAAEAfFfYpodzcXOXk5GjSpElKS0vTxo0bVVtbq6VLl0qyry2pr69XSUlJyH5FRUWaOnWqJkyY0GLOVatWKTU1VWPGjFEgEFBhYaFqamq0bt26Lh4WAADoS8IOLNnZ2Tp79qxeeeUVeb1eTZgwQbt27Qre9eP1elt8J4vf71d5ebnWrFnT6pxffvmlnnzySfl8PrlcLiUnJ6uyslJTpkzpwiEBAIC+xmFZltXTTXSHQCAgl8slv9+vyMjInm4H6LWOHDkit9utqqoqpaSk9HQ7xuJ9ArpHZz+/+S0hAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxuhRY1q9fr9GjRysiIkJut1sHDhxos3bfvn1yOBwtHsePHw+pKy8vV1JSkpxOp5KSkrRt27autAYAAPqgsANLWVmZVqxYoeeff17V1dWaNm2asrKyVFtb2+5+n376qbxeb/AxZsyY4HMej0fZ2dnKycnR0aNHlZOTowULFujw4cPhHxEAAOhzwg4sb7zxhpYsWaInnnhC48aNU0FBgeLj47Vhw4Z29xs+fLhiYmKCj/79+wefKygo0OzZs5WXl6fExETl5eVp5syZKigoaHO+pqYmBQKBkAcAAOibwgosFy5cUFVVlTIzM0PGMzMzdejQoXb3TU5OVmxsrGbOnKmPPvoo5DmPx9Nizjlz5rQ7Z35+vlwuV/ARHx8fzqEAAIBeJKzAcubMGV26dEnR0dEh49HR0fL5fK3uExsbq40bN6q8vFxbt27V2LFjNXPmTFVWVgZrfD5fWHNKUl5envx+f/BRV1cXzqEAAIBeZEBXdnI4HCHblmW1GGs2duxYjR07Nridlpamuro6/eIXv9D06dO7NKckOZ1OOZ3OrrQPAAB6mbBWWIYNG6b+/fu3WPk4ffp0ixWS9qSmpurEiRPB7ZiYmOueEwAA9F1hBZZBgwbJ7XaroqIiZLyiokLp6emdnqe6ulqxsbHB7bS0tBZz7tmzJ6w5AQBA3xX2KaHc3Fzl5ORo0qRJSktL08aNG1VbW6ulS5dKsq8tqa+vV0lJiST7DqBRo0Zp/PjxunDhgjZt2qTy8nKVl5cH51y+fLmmT5+u1atX66GHHtKOHTu0d+9eHTx4sJsOEwAA9GZhB5bs7GydPXtWr7zyirxeryZMmKBdu3YpISFBkuT1ekO+k+XChQt65plnVF9fr8GDB2v8+PF6//33NXfu3GBNenq6tmzZohdeeEEvvvii7rrrLpWVlWnq1KndcIgAAKC3c1iWZfV0E90hEAjI5XLJ7/crMjKyp9sBeq0jR47I7XarqqpKKSkpPd2OsXifgO7R2c9vfksIAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8LgWW9evXa/To0YqIiJDb7daBAwfarN26datmz56tO+64Q5GRkUpLS9MHH3wQUlNcXCyHw9Hicf78+a60BwAA+piwA0tZWZlWrFih559/XtXV1Zo2bZqysrJUW1vban1lZaVmz56tXbt2qaqqSjNmzNC8efNUXV0dUhcZGSmv1xvyiIiI6NpRAQCAPmVAuDu88cYbWrJkiZ544glJUkFBgT744ANt2LBB+fn5LeoLCgpCtl977TXt2LFD7733npKTk4PjDodDMTExne6jqalJTU1Nwe1AIBDmkQAAgN4irBWWCxcuqKqqSpmZmSHjmZmZOnToUKfmuHz5ss6dO6eoqKiQ8YaGBiUkJGjEiBF68MEHW6zAXCs/P18ulyv4iI+PD+dQAABALxJWYDlz5owuXbqk6OjokPHo6Gj5fL5OzfH666+rsbFRCxYsCI4lJiaquLhYO3fuVGlpqSIiIpSRkaETJ060OU9eXp78fn/wUVdXF86hAACAXiTsU0KSffrmapZltRhrTWlpqV5++WXt2LFDw4cPD46npqYqNTU1uJ2RkaGUlBS9+eabKiwsbHUup9Mpp9PZlfYBAEAvE1ZgGTZsmPr3799iNeX06dMtVl2uVVZWpiVLlujXv/61Zs2a1W5tv379NHny5HZXWAAAwK0jrFNCgwYNktvtVkVFRch4RUWF0tPT29yvtLRUixcv1ubNm/XAAw90+DqWZammpkaxsbHhtAcAAPqosE8J5ebmKicnR5MmTVJaWpo2btyo2tpaLV26VJJ9bUl9fb1KSkok2WFl4cKFWrNmjVJTU4OrM4MHD5bL5ZIkrVq1SqmpqRozZowCgYAKCwtVU1OjdevWdddxAgCAXizswJKdna2zZ8/qlVdekdfr1YQJE7Rr1y4lJCRIkrxeb8h3srz99tu6ePGili1bpmXLlgXHFy1apOLiYknSl19+qSeffFI+n08ul0vJycmqrKzUlClTrvPwAABAX+CwLMvq6Sa6QyAQkMvlkt/vV2RkZE+3A/RaR44ckdvtVlVVlVJSUnq6HWPxPgHdo7Of3/yWEAAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeF0KLOvXr9fo0aMVEREht9utAwcOtFu/f/9+ud1uRURE6M4779Rbb73Voqa8vFxJSUlyOp1KSkrStm3butIaAADog8IOLGVlZVqxYoWef/55VVdXa9q0acrKylJtbW2r9adOndLcuXM1bdo0VVdX67nnntPTTz+t8vLyYI3H41F2drZycnJ09OhR5eTkaMGCBTp8+HDXjwwAAPQZDsuyrHB2mDp1qlJSUrRhw4bg2Lhx4zR//nzl5+e3qH/22We1c+dOHTt2LDi2dOlSHT16VB6PR5KUnZ2tQCCg3bt3B2vuv/9+3X777SotLW21j6amJjU1NQW3A4GA4uPj5ff7FRkZGc4hAbjKof179dSfz9GLL76oxMTELs/T1NSkzz//vBs76z5xcXFyOp3XNcepU6f0wgsvqGjHASVPSe+mzoBbTyAQkMvl6vDze0A4k164cEFVVVX6yU9+EjKemZmpQ4cOtbqPx+NRZmZmyNicOXNUVFSkr7/+WgMHDpTH49HKlStb1BQUFLTZS35+vlatWhVO+wA64X//86CO/OW3pNN/L52+vrnu7ZaOboC6659inKS5f/kt1Vpnr38yAB0KK7CcOXNGly5dUnR0dMh4dHS0fD5fq/v4fL5W6y9evKgzZ84oNja2zZq25pSkvLw85ebmBrebV1gAXJ9p31uibdukUaNGKSIiosvz9PUVFkn65je/qZHJM7uhIwAdCSuwNHM4HCHblmW1GOuo/trxcOd0Op3d8h8cAKGGxcbre3/1crfMdW+3zAIAYV50O2zYMPXv37/Fysfp06dbrJA0i4mJabV+wIABGjp0aLs1bc0JAABuLWEFlkGDBsntdquioiJkvKKiQunprV90lpaW1qJ+z549mjRpkgYOHNhuTVtzAgCAW0vYp4Ryc3OVk5OjSZMmKS0tTRs3blRtba2WLl0qyb62pL6+XiUlJZLsO4LWrl2r3Nxc/ehHP5LH41FRUVHI3T/Lly/X9OnTtXr1aj300EPasWOH9u7dq4MHD3bTYQIAgN4s7MCSnZ2ts2fP6pVXXpHX69WECRO0a9cuJSQkSJK8Xm/Id7KMHj1au3bt0sqVK7Vu3TrFxcWpsLBQDz/8cLAmPT1dW7Zs0QsvvKAXX3xRd911l8rKyjR16tRuOEQAANDbhf09LKbq7H3cAADAHJ39/Oa3hAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA43Xp15pN1Pz9d4FAoIc7AQAAndX8ud3R99j2mcBy7tw5SVJ8fHwPdwIAAMJ17tw5uVyuNp/vM1/Nf/nyZX3++ecaMmSIHA5HT7cDoBsFAgHFx8errq6On94A+hjLsnTu3DnFxcWpX7+2r1TpM4EFQN/Fb4UB4KJbAABgPAILAAAwHoEFgPGcTqd++tOfyul09nQrAHoI17AAAADjscICAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAxqqsrNS8efMUFxcnh8Oh7du393RLAHoIgQWAsRobGzVx4kStXbu2p1sB0MP6zK81A+h7srKylJWV1dNtADAAKywAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIzHXUIAjNXQ0KCTJ08Gt0+dOqWamhpFRUVp5MiRPdgZgJvNYVmW1dNNAEBr9u3bpxkzZrQYX7RokYqLi29+QwB6DIEFAAAYj2tYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8/w+m6KVKCeFxsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_col = titanic_data.select_dtypes(include=['float', 'int'])\n",
    "for col in num_col:\n",
    "    plt.figure()\n",
    "    plt.boxplot(titanic_data[col])\n",
    "    plt.title('Boxplot of {}'.format(col), color = 'r')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e4fa5e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      int64\n",
       "Pclass        int64\n",
       "Sex          object\n",
       "Age         float64\n",
       "SibSp         int64\n",
       "Parch       float64\n",
       "Fare        float64\n",
       "Embarked     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4983983c",
   "metadata": {},
   "source": [
    "### Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9cd79f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.Sex.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "53a7e3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.Embarked.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2125fa94",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data1 = pd.get_dummies(titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b7fd820b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.1563</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  Sex_female  Sex_male  \\\n",
       "0         0       3  22.0      1    0.0   7.2500           0         1   \n",
       "1         1       1  38.0      1    0.0  63.1563           1         0   \n",
       "2         1       3  26.0      0    0.0   7.9250           1         0   \n",
       "3         1       1  35.0      1    0.0  53.1000           1         0   \n",
       "4         0       3  35.0      0    0.0   8.0500           0         1   \n",
       "\n",
       "   Embarked_C  Embarked_Q  Embarked_S  \n",
       "0           0           0           1  \n",
       "1           1           0           0  \n",
       "2           0           0           1  \n",
       "3           0           0           1  \n",
       "4           0           0           1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a991b154",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = titanic_data1.drop('Survived', axis = 1)  # DataFrame with feature columns\n",
    "y = titanic_data1['Survived']                 # Target column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9d8639",
   "metadata": {},
   "source": [
    "### Scailing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c963ba49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a7887e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bb50136f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = min_max.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4ae86904",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_female',\n",
       "       'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "03d5453d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(x, columns = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Sex_female',\n",
    "       'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "000cb0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3948f41",
   "metadata": {},
   "source": [
    "##  SVM Model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25bb643",
   "metadata": {},
   "source": [
    "###  1. Hold Out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e8057ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svmclf =SVC(kernel='linear')\n",
    "svmclf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c75cd393",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svm=svmclf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e040dc7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_svm = svmclf.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8cd3a3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8537735849056604"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_svm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943fc284",
   "metadata": {},
   "source": [
    "###  2. KFold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1e6ea59a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a92528e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold_validator = KFold(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d13e9dc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training Index: [169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186\n",
      " 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204\n",
      " 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222\n",
      " 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240\n",
      " 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258\n",
      " 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276\n",
      " 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294\n",
      " 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312\n",
      " 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330\n",
      " 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348\n",
      " 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366\n",
      " 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384\n",
      " 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402\n",
      " 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420\n",
      " 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438\n",
      " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
      " 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474\n",
      " 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492\n",
      " 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510\n",
      " 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528\n",
      " 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546\n",
      " 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564\n",
      " 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
      " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
      " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
      " 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636\n",
      " 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654\n",
      " 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672\n",
      " 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168]\n",
      "training Index: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 338 339 340 341 342 343 344 345 346 347 348\n",
      " 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366\n",
      " 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384\n",
      " 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402\n",
      " 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420\n",
      " 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438\n",
      " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
      " 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474\n",
      " 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492\n",
      " 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510\n",
      " 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528\n",
      " 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546\n",
      " 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564\n",
      " 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
      " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
      " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
      " 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636\n",
      " 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654\n",
      " 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672\n",
      " 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index [169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186\n",
      " 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204\n",
      " 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222\n",
      " 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240\n",
      " 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258\n",
      " 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276\n",
      " 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294\n",
      " 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312\n",
      " 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330\n",
      " 331 332 333 334 335 336 337]\n",
      "training Index: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 507 508 509 510\n",
      " 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528\n",
      " 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546\n",
      " 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564\n",
      " 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
      " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
      " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
      " 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636\n",
      " 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654\n",
      " 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672\n",
      " 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index [338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355\n",
      " 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373\n",
      " 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391\n",
      " 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409\n",
      " 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427\n",
      " 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445\n",
      " 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463\n",
      " 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481\n",
      " 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499\n",
      " 500 501 502 503 504 505 506]\n",
      "training Index: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
      " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
      " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
      " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
      " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
      " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
      " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
      " 504 505 506 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index [507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524\n",
      " 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542\n",
      " 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560\n",
      " 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578\n",
      " 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596\n",
      " 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614\n",
      " 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632\n",
      " 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650\n",
      " 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668\n",
      " 669 670 671 672 673 674 675]\n",
      "training Index: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
      " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
      " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
      " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
      " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
      " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
      " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
      " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
      " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
      " 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
      " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
      " 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593\n",
      " 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611\n",
      " 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629\n",
      " 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647\n",
      " 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665\n",
      " 666 667 668 669 670 671 672 673 674 675]\n",
      "Testing Index [676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693\n",
      " 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711\n",
      " 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729\n",
      " 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747\n",
      " 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765\n",
      " 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783\n",
      " 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801\n",
      " 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819\n",
      " 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837\n",
      " 838 839 840 841 842 843 844]\n"
     ]
    }
   ],
   "source": [
    "for train_index,test_index in kfold_validator.split(x,y):\n",
    "    print('training Index:',train_index)\n",
    "    print('Testing Index',test_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ac986d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "01308128",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_result = cross_val_score(svmclf,x,y,cv=kfold_validator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9376fd89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81656805, 0.79881657, 0.79881657, 0.74556213, 0.81065089])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "21274eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7940828402366863"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cv_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29d99c35",
   "metadata": {},
   "source": [
    "###  3. Stratified KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1a1e28cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "04161b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "skfold_validator = StratifiedKFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c615dad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Index:  [158 159 160 161 162 163 164 167 170 171 173 178 179 181 182 183 184 186\n",
      " 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204\n",
      " 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222\n",
      " 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240\n",
      " 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258\n",
      " 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276\n",
      " 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294\n",
      " 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312\n",
      " 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330\n",
      " 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348\n",
      " 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366\n",
      " 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384\n",
      " 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402\n",
      " 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420\n",
      " 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438\n",
      " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
      " 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474\n",
      " 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492\n",
      " 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510\n",
      " 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528\n",
      " 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546\n",
      " 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564\n",
      " 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
      " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
      " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
      " 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636\n",
      " 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654\n",
      " 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672\n",
      " 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index:  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 165 166 168 169\n",
      " 172 174 175 176 177 180 185]\n",
      "Training Index:  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 165 166 168 169\n",
      " 172 174 175 176 177 180 185 338 339 340 341 342 343 344 345 346 347 348\n",
      " 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366\n",
      " 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384\n",
      " 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402\n",
      " 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420\n",
      " 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438\n",
      " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
      " 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474\n",
      " 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492\n",
      " 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510\n",
      " 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528\n",
      " 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546\n",
      " 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564\n",
      " 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
      " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
      " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
      " 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636\n",
      " 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654\n",
      " 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672\n",
      " 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index:  [158 159 160 161 162 163 164 167 170 171 173 178 179 181 182 183 184 186\n",
      " 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204\n",
      " 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222\n",
      " 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240\n",
      " 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258\n",
      " 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276\n",
      " 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294\n",
      " 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312\n",
      " 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330\n",
      " 331 332 333 334 335 336 337]\n",
      "Training Index:  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 504 506 508 509\n",
      " 510 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528\n",
      " 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546\n",
      " 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564\n",
      " 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582\n",
      " 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600\n",
      " 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618\n",
      " 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636\n",
      " 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654\n",
      " 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672\n",
      " 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index:  [338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355\n",
      " 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373\n",
      " 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391\n",
      " 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409\n",
      " 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427\n",
      " 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445\n",
      " 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463\n",
      " 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481\n",
      " 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499\n",
      " 500 501 502 503 505 507 511]\n",
      "Training Index:  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
      " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
      " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
      " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
      " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
      " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
      " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
      " 505 507 511 670 671 672 673 675 679 680 683 684 685 686 687 688 689 690\n",
      " 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708\n",
      " 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726\n",
      " 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744\n",
      " 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762\n",
      " 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780\n",
      " 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798\n",
      " 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816\n",
      " 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834\n",
      " 835 836 837 838 839 840 841 842 843 844]\n",
      "Testing Index:  [504 506 508 509 510 512 513 514 515 516 517 518 519 520 521 522 523 524\n",
      " 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542\n",
      " 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560\n",
      " 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578\n",
      " 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596\n",
      " 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614\n",
      " 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632\n",
      " 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650\n",
      " 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668\n",
      " 669 674 676 677 678 681 682]\n",
      "Training Index:  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
      " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
      " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
      " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
      " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
      " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
      " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
      " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
      " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
      " 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
      " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
      " 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593\n",
      " 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611\n",
      " 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629\n",
      " 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647\n",
      " 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665\n",
      " 666 667 668 669 674 676 677 678 681 682]\n",
      "Testing Index:  [670 671 672 673 675 679 680 683 684 685 686 687 688 689 690 691 692 693\n",
      " 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711\n",
      " 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729\n",
      " 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747\n",
      " 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765\n",
      " 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783\n",
      " 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801\n",
      " 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819\n",
      " 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837\n",
      " 838 839 840 841 842 843 844]\n"
     ]
    }
   ],
   "source": [
    "for train_index,test_index in skfold_validator.split(x,y):\n",
    "    print('Training Index: ',train_index)\n",
    "    print('Testing Index: ',test_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b35d2455",
   "metadata": {},
   "outputs": [],
   "source": [
    "skcv_result =cross_val_score(svmclf,x,y,cv=skfold_validator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7d742dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80473373, 0.81065089, 0.79881657, 0.76331361, 0.79289941])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skcv_result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3777b4b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7940828402366864"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(skcv_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c19f3a",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac7bb1e",
   "metadata": {},
   "source": [
    "### 1. Hold Out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "21a2994f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "\n",
    "k_val = np.arange(3,25)\n",
    "acc_val = []\n",
    "for k in k_val:\n",
    "    classifier = KNeighborsClassifier(n_neighbors = k,metric = 'minkowski')\n",
    "    classifier.fit(x_train, y_train)\n",
    "    pred  = classifier.predict(x_test)\n",
    "    acc = accuracy_score(y_test,pred)\n",
    "    acc_val.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9d451ea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k_val,acc_val,'o-')\n",
    "plt.title('kNN Accuracy Plot')\n",
    "plt.xlabel('k value')\n",
    "plt.ylabel('accuracy')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bde296a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=24)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors =24 , metric = 'minkowski')\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f29f378f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn  = classifier.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5d52f114",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_knn=round(accuracy_score(y_test,y_pred_knn),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e9511847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is:  0.88\n"
     ]
    }
   ],
   "source": [
    "print('accuracy is: ',score_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b5dec33",
   "metadata": {},
   "source": [
    "### 2.K-Fold "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f0a69e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_result1 = cross_val_score(classifier,x,y,cv=kfold_validator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "93e1151f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81656805, 0.79289941, 0.81065089, 0.74556213, 0.81656805])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_result1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb4532f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7964497041420119"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cv_result1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c79a7ca",
   "metadata": {},
   "source": [
    "### 3.Stratified K-Fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "48f4f88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "skcv_result1 =cross_val_score(classifier,x,y,cv=skfold_validator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f533d1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79881657, 0.79881657, 0.81065089, 0.77514793, 0.80473373])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skcv_result1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b781ebb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7976331360946746"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(skcv_result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cbcadfc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kNN</td>\n",
       "      <td>0.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.853774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>kNN Stratified K-Fold</td>\n",
       "      <td>0.797633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kNN K-Fold</td>\n",
       "      <td>0.796450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM Stratified K-Fold</td>\n",
       "      <td>0.794083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM K-Fold</td>\n",
       "      <td>0.794083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model     Score\n",
       "3                    kNN  0.880000\n",
       "0                    SVM  0.853774\n",
       "5  kNN Stratified K-Fold  0.797633\n",
       "4             kNN K-Fold  0.796450\n",
       "2  SVM Stratified K-Fold  0.794083\n",
       "1             SVM K-Fold  0.794083"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Model': ['SVM','SVM K-Fold','SVM Stratified K-Fold', 'kNN','kNN K-Fold','kNN Stratified K-Fold'],\n",
    "    'Score': [score_svm,np.mean(cv_result),np.mean(skcv_result),score_knn,np.mean(cv_result1),np.mean(skcv_result1)]})\n",
    "\n",
    "results= results.sort_values(by='Score', ascending=False)\n",
    "\n",
    "results.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84896c49",
   "metadata": {},
   "source": [
    "### insights:\n",
    "####  According to the table, the kNN model has a score of 0.880000, which is higher than the score of the SVM model, which is 0.853774. This suggests that the kNN model may be a better choice for this particular problem. However, it is important to note that the scores for both models are relatively high, so either model could be a good choice.The kNN model has a slightly higher score than the SVM model, but the difference is not statistically significant. This suggests that both models are likely to be effective at classifying data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "861bc1bc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}