SVM-Project-Exercise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Авторские права принадлежат Pierian Data Inc.</em></center>\n",
    "<center><em>Для дополнительной информации посетите наш сайт <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Метод опорных векторов - Support Vector Machines \n",
    "## Упражнения\n",
    "\n",
    "## Подделка вина\n",
    "\n",
    "Чаще всего вино подделывают, разбавляя его более дешёвыми продуктами (например, соком), а иногда и с помощью опасных химикатов или подсластителей (чтобы добавить цвет или вкус).\n",
    "\n",
    "Другой тип подделки - продажа более дешёвых вин под более дорогими марками.\n",
    "\n",
    "<img src=\"wine.jpg\">\n",
    "\n",
    "## Цели проекта\n",
    "\n",
    "Компания-дистрибьютор вина недавно столкнулась с подделками. В итоге был проведён аудит различных вин с помощью химического анализа. Компания занимается экспортом очень качественных и дорогих вин, но один из поставщиков попытался передать дешёвое вино под видом более дорогого. Компания-дистрибьютор наняла Вас, чтобы Вы создали модель машинного обучения, которая предскажет низкое качество вина (то есть, \"подделку\"). Они хотят узнать, возможно ли определить разницу между дешёвыми и дорогими винами.\n",
    "\n",
    "\n",
    "Источник данных: *P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties.\n",
    "In Decision Support Systems, Elsevier, 47(4):547-553, 2009.*\n",
    "\n",
    "---\n",
    "---\n",
    "\n",
    "**ЗАДАНИЕ: Обшая цель - используя данные ниже, разработайте модель машинного обучения, которая будет предсказывать на основе некоторых химических тестов, является ли вино настоящим или поддельным. Выполните задания ниже.**\n",
    "\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выполните задания, написанные жирным шрифтом\n",
    "\n",
    "**ЗАДАНИЕ: запустите ячейки ниже, чтобы импортировать библиотеки и загрузить данные.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../DATA/wine_fraud.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Legit</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Legit</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol quality type  \n",
       "0      9.4   Legit  red  \n",
       "1      9.8   Legit  red  \n",
       "2      9.8   Legit  red  \n",
       "3      9.8   Legit  red  \n",
       "4      9.4   Legit  red  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Сколько различных значений находится в целевой колонке quality, значения которой мы будем предсказывать?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Legit', 'Fraud'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Нарисуйте график countplot, отображающий количество для каждой из категорий - Legit и Fraud.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='quality', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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113odCwoKUufOna3jycnJSktLU6dOnWS32/XYY4/J6XRq8ODBkqSRI0cqNjZWY8eO1fz58+VyuTR79mylpKRYt7wmT56sl19+WTNnztSECRO0efNmrV69Wrm5uc27YAAA0GL5LBD9HIsWLZKfn5+SkpJUWVmphIQELV261Bpv06aN1q9frylTpsjpdCooKEjjx4/XvHnzrJqePXsqNzdX06ZNU1ZWlrp27arXX39dCQkJvlgSAABogWx1dXV1vm6ipfN4PHI4HHK73bLb7U12nrgZbzbZ3EBrVrRgnK9bANAKXcrvb5+/hwgAAMDXCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4zUoEA0fPlwVFRUXHPd4PBo+fPgv7QkAAKBZNSgQbdmyRVVVVRccP3v2rD766KNf3BQAAEBzanspxZ9//rn1771798rlcln7NTU1ysvL069+9avG6w4AAKAZXFIg6tevn2w2m2w220VvjQUGBuqll15qtOYAAACawyUFooMHD6qurk5XXnmldu3apdDQUGvM399fYWFhatOmTaM3CQAA0JQuKRB1795dklRbW9skzQAAAPjCJQWi8+3fv18ffvihjh49ekFAmjNnzi9uDAAAoLk0KBC99tprmjJlirp06aKIiAjZbDZrzGazEYgAAECr0qBA9Mwzz+jZZ5/VrFmzGrsfAACAZteg9xCdOHFC9913X2P3AgAA4BMNCkT33XefNm7c2Ni9AAAA+ESDbpnFxMToySef1I4dO9S3b1+1a9fOa/x3v/tdozQHAADQHBoUiF599VUFBweroKBABQUFXmM2m41ABAAAWpUGBaKDBw82dh8AAAA+06BniAAAAC4nDbpCNGHChB8dX758eYOaAQAA8IUGBaITJ0547VdXV+uLL75QRUXFRf/oKwAAQEvWoEC0Zs2aC47V1tZqypQp+vWvf/2LmwIAAGhOjfYMkZ+fn9LS0rRo0aLGmhIAAKBZNOpD1QcOHNC5c+cac0oAAIAm16BbZmlpaV77dXV1OnLkiHJzczV+/PhGaQwAAKC5NCgQffbZZ177fn5+Cg0NVWZm5k9+Aw0AAKClaVAg+vDDDxu7DwAAAJ9pUCCqd+zYMZWWlkqSevXqpdDQ0EZpCgAAoDk16KHq06dPa8KECYqMjNSQIUM0ZMgQRUVFKTk5Wd99911j9wgAANCkGhSI0tLSVFBQoHXr1qmiokIVFRV67733VFBQoMcff7yxewQAAGhSDbpl9p//+Z/6j//4Dw0dOtQ6dttttykwMFC/+c1vtGzZssbqDwAAoMk16ArRd999p/Dw8AuOh4WFccsMAAC0Og0KRE6nU0899ZTOnj1rHTtz5oz+8Ic/yOl0NlpzAAAAzaFBgWjx4sXatm2bunbtqhEjRmjEiBGKjo7Wtm3blJWV9bPnWbZsma677jrZ7XbZ7XY5nU598MEH1vjZs2eVkpKizp07Kzg4WElJSSovL/eao6ysTImJierQoYPCwsI0Y8aMC96WvWXLFg0YMEABAQGKiYlRTk5OQ5YNAAAuUw0KRH379tX+/fuVkZGhfv36qV+/fnr++ef19ddf65prrvnZ83Tt2lXPP/+8ioqK9Mknn2j48OG68847VVJSIkmaNm2a1q1bp3fffVcFBQU6fPiw7rnnHuvzNTU1SkxMVFVVlbZv366VK1cqJydHc+bMsWoOHjyoxMREDRs2TMXFxUpNTdUjjzyiDRs2NGTpAADgMmSrq6uru9QPZWRkKDw8/IK3Ui9fvlzHjh3TrFmzGtxQp06dtGDBAt17770KDQ3VqlWrdO+990qS9u3bpz59+qiwsFCDBw/WBx98oNtvv12HDx+2nmnKzs7WrFmzdOzYMfn7+2vWrFnKzc3VF198YZ1j9OjRqqioUF5e3s/qyePxyOFwyO12y263N3htPyVuxptNNjfQmhUtGOfrFgC0Qpfy+7tBV4j++Mc/qnfv3hccv+aaa5Sdnd2QKVVTU6O3335bp0+fltPpVFFRkaqrqxUfH2/V9O7dW926dVNhYaEkqbCwUH379vV6wDshIUEej8e6ylRYWOg1R31N/RwXU1lZKY/H47UBAIDLV4MCkcvlUmRk5AXHQ0NDdeTIkUuaa8+ePQoODlZAQIAmT56sNWvWKDY2Vi6XS/7+/goJCfGqDw8Pl8vlsvr4/rfd6vd/qsbj8ejMmTMX7SkjI0MOh8PaoqOjL2lNAACgdWlQIKp/gPr7tm3bpqioqEuaq1evXiouLtbOnTs1ZcoUjR8/Xnv37m1IW40mPT1dbrfb2g4dOuTTfgAAQNNq0IsZJ06cqNTUVFVXV2v48OGSpE2bNmnmzJmX/KZqf39/xcTESJLi4uK0e/duZWVl6f7771dVVZUqKiq8rhKVl5crIiJCkhQREaFdu3Z5zVf/LbTza77/zbTy8nLZ7XYFBgZetKeAgAAFBARc0joAAEDr1aBANGPGDP3tb3/To48+qqqqKklS+/btNWvWLKWnp/+ihmpra1VZWam4uDi1a9dOmzZtUlJSkiSptLRUZWVl1ruOnE6nnn32WR09elRhYWGSpPz8fNntdsXGxlo1//3f/+11jvz8fN6XBAAALA0KRDabTS+88IKefPJJffnllwoMDNRVV111yVdV0tPTNWrUKHXr1k0nT57UqlWrtGXLFm3YsEEOh0PJyclKS0tTp06dZLfb9dhjj8npdGrw4MGSpJEjRyo2NlZjx47V/Pnz5XK5NHv2bKWkpFi9TJ48WS+//LJmzpypCRMmaPPmzVq9erVyc3MbsnQAAHAZalAgqhccHKwbbrihwZ8/evSoxo0bpyNHjsjhcOi6667Thg0b9E//9E+SpEWLFsnPz09JSUmqrKxUQkKCli5dan2+TZs2Wr9+vaZMmSKn06mgoCCNHz9e8+bNs2p69uyp3NxcTZs2TVlZWeratatef/11JSQkNHzhAADgstKg9xCZhvcQAb7Fe4gANESTv4cIAADgckIgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHg+DUQZGRm64YYb1LFjR4WFhemuu+5SaWmpV83Zs2eVkpKizp07Kzg4WElJSSovL/eqKSsrU2Jiojp06KCwsDDNmDFD586d86rZsmWLBgwYoICAAMXExCgnJ6eplwcAAFoJnwaigoICpaSkaMeOHcrPz1d1dbVGjhyp06dPWzXTpk3TunXr9O6776qgoECHDx/WPffcY43X1NQoMTFRVVVV2r59u1auXKmcnBzNmTPHqjl48KASExM1bNgwFRcXKzU1VY888og2bNjQrOsFAAAtk62urq7O103UO3bsmMLCwlRQUKAhQ4bI7XYrNDRUq1at0r333itJ2rdvn/r06aPCwkINHjxYH3zwgW6//XYdPnxY4eHhkqTs7GzNmjVLx44dk7+/v2bNmqXc3Fx98cUX1rlGjx6tiooK5eXl/WRfHo9HDodDbrdbdru9aRYvKW7Gm002N9CaFS0Y5+sWALRCl/L7u0U9Q+R2uyVJnTp1kiQVFRWpurpa8fHxVk3v3r3VrVs3FRYWSpIKCwvVt29fKwxJUkJCgjwej0pKSqya8+eor6mf4/sqKyvl8Xi8NgAAcPlqMYGotrZWqampuummm3TttddKklwul/z9/RUSEuJVGx4eLpfLZdWcH4bqx+vHfqzG4/HozJkzF/SSkZEhh8NhbdHR0Y2yRgAA0DK1mECUkpKiL774Qm+//bavW1F6errcbre1HTp0yNctAQCAJtTW1w1I0tSpU7V+/Xpt3bpVXbt2tY5HRESoqqpKFRUVXleJysvLFRERYdXs2rXLa776b6GdX/P9b6aVl5fLbrcrMDDwgn4CAgIUEBDQKGsDAAAtn0+vENXV1Wnq1Klas2aNNm/erJ49e3qNx8XFqV27dtq0aZN1rLS0VGVlZXI6nZIkp9OpPXv26OjRo1ZNfn6+7Ha7YmNjrZrz56ivqZ8DAACYzadXiFJSUrRq1Sq999576tixo/XMj8PhUGBgoBwOh5KTk5WWlqZOnTrJbrfrsccek9Pp1ODBgyVJI0eOVGxsrMaOHav58+fL5XJp9uzZSklJsa7yTJ48WS+//LJmzpypCRMmaPPmzVq9erVyc3N9tnYAANBy+PQK0bJly+R2uzV06FBFRkZa2zvvvGPVLFq0SLfffruSkpI0ZMgQRURE6L/+67+s8TZt2mj9+vVq06aNnE6nfvvb32rcuHGaN2+eVdOzZ0/l5uYqPz9f119/vTIzM/X6668rISGhWdcLAABaphb1HqKWivcQAb7Fe4gANESrfQ8RAACALxCIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGM+ngWjr1q264447FBUVJZvNprVr13qN19XVac6cOYqMjFRgYKDi4+O1f/9+r5rjx49rzJgxstvtCgkJUXJysk6dOuVV8/nnn+uWW25R+/btFR0drfnz5zf10gAAQCvi00B0+vRpXX/99XrllVcuOj5//nwtWbJE2dnZ2rlzp4KCgpSQkKCzZ89aNWPGjFFJSYny8/O1fv16bd26VZMmTbLGPR6PRo4cqe7du6uoqEgLFizQ3Llz9eqrrzb5+gAAQOvQ1pcnHzVqlEaNGnXRsbq6Oi1evFizZ8/WnXfeKUl68803FR4errVr12r06NH68ssvlZeXp927d2vgwIGSpJdeekm33XabXnzxRUVFRemtt95SVVWVli9fLn9/f11zzTUqLi7WwoULvYLT+SorK1VZWWntezyeRl45AABoSVrsM0QHDx6Uy+VSfHy8dczhcGjQoEEqLCyUJBUWFiokJMQKQ5IUHx8vPz8/7dy506oZMmSI/P39rZqEhASVlpbqxIkTFz13RkaGHA6HtUVHRzfFEgEAQAvRYgORy+WSJIWHh3sdDw8Pt8ZcLpfCwsK8xtu2batOnTp51VxsjvPP8X3p6elyu93WdujQoV++IAAA0GL59JZZSxUQEKCAgABftwEAAJpJi71CFBERIUkqLy/3Ol5eXm6NRURE6OjRo17j586d0/Hjx71qLjbH+ecAAABma7GBqGfPnoqIiNCmTZusYx6PRzt37pTT6ZQkOZ1OVVRUqKioyKrZvHmzamtrNWjQIKtm69atqq6utmry8/PVq1cvXXHFFc20GgAA0JL5NBCdOnVKxcXFKi4ulvT3B6mLi4tVVlYmm82m1NRUPfPMM3r//fe1Z88ejRs3TlFRUbrrrrskSX369NGtt96qiRMnateuXdq2bZumTp2q0aNHKyoqSpL04IMPyt/fX8nJySopKdE777yjrKwspaWl+WjVAACgpfHpM0SffPKJhg0bZu3Xh5Tx48crJydHM2fO1OnTpzVp0iRVVFTo5ptvVl5entq3b2995q233tLUqVM1YsQI+fn5KSkpSUuWLLHGHQ6HNm7cqJSUFMXFxalLly6aM2fOD37lHgAAmMdWV1dX5+smWjqPxyOHwyG32y273d5k54mb8WaTzQ20ZkULxvm6BQCt0KX8/m6xzxABAAA0FwIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABivra8bAAATlM3r6+sWgBap25w9vm5BEleIAAAACEQAAAAEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8YwKRK+88op69Oih9u3ba9CgQdq1a5evWwIAAC2AMYHonXfeUVpamp566il9+umnuv7665WQkKCjR4/6ujUAAOBjxgSihQsXauLEiXr44YcVGxur7OxsdejQQcuXL/d1awAAwMeM+OOuVVVVKioqUnp6unXMz89P8fHxKiwsvKC+srJSlZWV1r7b7ZYkeTyeJu2zpvJMk84PtFZN/bPXHE6erfF1C0CL1JQ/3/Vz19XV/WStEYHor3/9q2pqahQeHu51PDw8XPv27bugPiMjQ3/4wx8uOB4dHd1kPQL4YY6XJvu6BQBNJcPR5Kc4efKkHI4fP48RgehSpaenKy0tzdqvra3V8ePH1blzZ9lsNh92hubg8XgUHR2tQ4cOyW63+7odAI2In2+z1NXV6eTJk4qKivrJWiMCUZcuXdSmTRuVl5d7HS8vL1dERMQF9QEBAQoICPA6FhIS0pQtogWy2+38DxO4TPHzbY6fujJUz4iHqv39/RUXF6dNmzZZx2pra7Vp0yY5nU4fdgYAAFoCI64QSVJaWprGjx+vgQMH6sYbb9TixYt1+vRpPfzww75uDQAA+Jgxgej+++/XsWPHNGfOHLlcLvXr1095eXkXPGgNBAQE6KmnnrrgtimA1o+fb/wQW93P+S4aAADAZcyIZ4gAAAB+DIEIAAAYj0AEAACMRyACGqhHjx5avHixr9sA0Mweeugh3XXXXb5uA42MQITLVlP/T2v37t2aNGmStW+z2bR27domOx9guoceekg2m+2C7euvv/Z1a7gMGPO1e6CxhYaG+roFwDi33nqrVqxY4XXs+z+LVVVV8vf3b862cBngChGM9MUXX2jUqFEKDg5WeHi4xo4dq7/+9a/W+MmTJzVmzBgFBQUpMjJSixYt0tChQ5WammrVnH/LrEePHpKku+++WzabzdoH0LgCAgIUERHhtY0YMUJTp05VamqqunTpooSEBEnSwoUL1bdvXwUFBSk6OlqPPvqoTp06Zc01d+5c9evXz2v+xYsXe/381tTUKC0tTSEhIercubNmzpz5s/5yOlofAhGMU1FRoeHDh6t///765JNPlJeXp/Lycv3mN7+xatLS0rRt2za9//77ys/P10cffaRPP/30B+fcvXu3JGnFihU6cuSItQ+geaxcuVL+/v7atm2bsrOzJUl+fn5asmSJSkpKtHLlSm3evFkzZ868pHkzMzOVk5Oj5cuX6+OPP9bx48e1Zs2aplgCfIxbZjDOyy+/rP79++u5556zji1fvlzR0dH66quvFBkZqZUrV2rVqlUaMWKEpL8HnR/7a8n1l+xDQkIu+geDATSO9evXKzg42NofNWqUJOmqq67S/PnzvWq/f0X3mWee0eTJk7V06dKffb7FixcrPT1d99xzjyQpOztbGzZs+AUrQEtFIIJx/vznP+vDDz/0+p9qvQMHDujMmTOqrq7WjTfeaB13OBzq1atXc7YJ4CKGDRumZcuWWftBQUF64IEHFBcXd0Htn/70J2VkZGjfvn3yeDw6d+6czp49q++++04dOnT4yXO53W4dOXJEgwYNso61bdtWAwcO5LbZZYhABOOcOnVKd9xxh1544YULxiIjI/nGCtCCBQUFKSYm5qLHz/ftt9/q9ttv15QpU/Tss8+qU6dO+vjjj5WcnKyqqip16NBBfn5+FwSb6urqJu0fLRfPEME4AwYMUElJiXr06KGYmBivLSgoSFdeeaXatWvn9RyQ2+3WV1999aPztmvXTjU1NU3dPoCfoaioSLW1tcrMzNTgwYN19dVX6/Dhw141oaGhcrlcXqGouLjY+rfD4VBkZKR27txpHTt37pyKioqavH80PwIRLmtut1vFxcVe26RJk3T8+HE98MAD2r17tw4cOKANGzbo4YcfVk1NjTp27Kjx48drxowZ+vDDD1VSUqLk5GT5+fnJZrP94Ll69OihTZs2yeVy6cSJE824SgDfFxMTo+rqar300kv65ptv9K//+q/Ww9b1hg4dqmPHjmn+/Pk6cOCAXnnlFX3wwQdeNb///e/1/PPPa+3atdq3b58effRRVVRUNONK0FwIRLisbdmyRf379/fann76aW3btk01NTUaOXKk+vbtq9TUVIWEhMjP7+8/EgsXLpTT6dTtt9+u+Ph43XTTTerTp4/at2//g+fKzMxUfn6+oqOj1b9//+ZaIoCLuP7667Vw4UK98MILuvbaa/XWW28pIyPDq6ZPnz5aunSpXnnlFV1//fXatWuXpk+f7lXz+OOPa+zYsRo/frycTqc6duyou+++uzmXgmZiq+PJMOAnnT59Wr/61a+UmZmp5ORkX7cDAGhkPFQNXMRnn32mffv26cYbb5Tb7da8efMkSXfeeaePOwMANAUCEfADXnzxRZWWlsrf319xcXH66KOP1KVLF1+3BQBoAtwyAwAAxuOhagAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAcB55s6dq379+ln7Dz30kO666y6f9QOgefAeIgD4EVlZWV5//HPo0KHq16+fFi9e7LumADQ6AhEA/AiHw+HrFgA0A26ZAWg1Tp8+rXHjxik4OFiRkZHKzMzU0KFDlZqaKkmy2Wxau3at12dCQkKUk5Nj7c+aNUtXX321OnTooCuvvFJPPvmkqqurf/Cc598ye+ihh1RQUKCsrCzZbDbZbDYdPHhQMTExevHFF70+V1xcLJvNpq+//roxlg6giRGIALQaM2bMUEFBgd577z1t3LhRW7Zs0aeffnpJc3Ts2FE5OTnau3evsrKy9Nprr2nRokU/67NZWVlyOp2aOHGijhw5oiNHjqhbt26aMGGCVqxY4VW7YsUKDRkyRDExMZfUHwDfIBABaBVOnTqlN954Qy+++KJGjBihvn37auXKlTp37twlzTN79mz9wz/8g3r06KE77rhD06dP1+rVq3/WZx0Oh/z9/dWhQwdFREQoIiJCbdq00UMPPaTS0lLt2rVLklRdXa1Vq1ZpwoQJl7xOAL7BM0QAWoUDBw6oqqpKgwYNso516tRJvXr1uqR53nnnHS1ZskQHDhzQqVOndO7cOdnt9l/UW1RUlBITE7V8+XLdeOONWrdunSorK3Xffff9onkBNB+uEAG4bNhsNn3/71Wf/3xQYWGhxowZo9tuu03r16/XZ599pieeeEJVVVW/+NyPPPKI3n77bZ05c0YrVqzQ/fffrw4dOvzieQE0D64QAWgVfv3rX6tdu3bauXOnunXrJkk6ceKEvvrqK/3jP/6jJCk0NFRHjhyxPrN//35999131v727dvVvXt3PfHEE9ax//mf/7mkPvz9/VVTU3PB8dtuu01BQUFatmyZ8vLytHXr1kuaF4BvEYgAtArBwcFKTk7WjBkz1LlzZ4WFhemJJ56Qn9//X+gePny4Xn75ZTmdTtXU1GjWrFlq166dNX7VVVeprKxMb7/9tm644Qbl5uZqzZo1l9RHjx49tHPnTn377bcKDg5Wp06d5OfnZz1LlJ6erquuukpOp7PR1g6g6XHLDECrsWDBAt1yyy264447FB8fr5tvvllxcXHWeGZmpqKjo3XLLbfowQcf1PTp071uW/3zP/+zpk2bpqlTp6pfv37avn27nnzyyUvqYfr06WrTpo1iY2MVGhqqsrIyayw5OVlVVVV6+OGHf/liATQrW933b7gDQCvSkt4c/dFHH2nEiBE6dOiQwsPDfd0OgEvALTMA+IUqKyt17NgxzZ07V/fddx9hCGiFuGUGAL/Qv//7v6t79+6qqKjQ/Pnzfd0OgAbglhkAADAeV4gAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOP9H9XHGQU68M3TAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='quality')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Давайте выясним, есть ли разница в количестве подделок в зависимости от того, красное вино или белое. Нарисуйте график countplot, отображая *тип* вина по оси x, и разбивая значения Fraud и Legit с помощью параметра hue.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='type', hue='quality')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Какой процент красных вин является подделкой? Какой процент белых вин является подделкой?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_perc = len(df[(df['quality'] == 'Fraud') & (df['type'] == 'red')]) / len(df[df['type'] == 'red']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_perc = len(df[(df['quality'] == 'Fraud') & (df['type'] == 'white')]) / len(df[df['type'] == 'white']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of fraud in Red Wines:\n",
      "3.9399624765478425\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of fraud in Red Wines:')\n",
    "print(red_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of fraud in White Wines:\n",
      "3.7362188648427925\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of fraud in White Wines:')\n",
    "print(white_perc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Вычислите корреляцию между различными признаками и колонкой \"quality\". Чтобы сделать это, Вам можете использовать значения 0 и 1 вместо текстовых значений Legit и Fraud.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fraud'] = df['quality'].map({'Legit': 0, 'Fraud': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Андрей\\AppData\\Local\\Temp\\ipykernel_2636\\1400609001.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Fraud']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "fixed acidity           0.021794\n",
       "volatile acidity        0.151228\n",
       "citric acid            -0.061789\n",
       "residual sugar         -0.048756\n",
       "chlorides               0.034499\n",
       "free sulfur dioxide    -0.085204\n",
       "total sulfur dioxide   -0.035252\n",
       "density                 0.016351\n",
       "pH                      0.020107\n",
       "sulphates              -0.034046\n",
       "alcohol                -0.051141\n",
       "Fraud                   1.000000\n",
       "Name: Fraud, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Fraud']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Нарисуйте график bar plot, отобразив значения корреляции для поддельных вин.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Андрей\\AppData\\Local\\Temp\\ipykernel_2636\\3472932135.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()['Fraud'][:-1].sort_values().plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Напишите Ваш код здесь\n",
    "df.corr()['Fraud'][:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Нарисуйте график clustermap с помощью seaborn, чтобы исследовать связи между переменными.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality', 'type', 'Fraud'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Андрей\\AppData\\Local\\Temp\\ipykernel_2636\\3512857711.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.clustermap(data=df.corr())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x205c45b5ad0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(data=df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Модель машинного обучения\n",
    "\n",
    "**ЗАДАНИЕ: Сконвертируйте категориальную колонку \"type\" из текстовых значений \"red\" и \"white\" в dummy-переменные:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Напишите Ваш код здесь\n",
    "dumm = pd.get_dummies(df['type'])\n",
    "df = pd.concat([df, dumm], axis=1)\n",
    "df.drop('type', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>Fraud</th>\n",
       "      <th>red</th>\n",
       "      <th>white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>Legit</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4              0.70         0.00             1.9      0.076   \n",
       "1               7.8              0.88         0.00             2.6      0.098   \n",
       "2               7.8              0.76         0.04             2.3      0.092   \n",
       "3              11.2              0.28         0.56             1.9      0.075   \n",
       "4               7.4              0.70         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "6492            6.2              0.21         0.29             1.6      0.039   \n",
       "6493            6.6              0.32         0.36             8.0      0.047   \n",
       "6494            6.5              0.24         0.19             1.2      0.041   \n",
       "6495            5.5              0.29         0.30             1.1      0.022   \n",
       "6496            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "6492                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "6493                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "6494                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "6495                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "6496                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol quality  Fraud  red  white  \n",
       "0         9.4   Legit      0    1      0  \n",
       "1         9.8   Legit      0    1      0  \n",
       "2         9.8   Legit      0    1      0  \n",
       "3         9.8   Legit      0    1      0  \n",
       "4         9.4   Legit      0    1      0  \n",
       "...       ...     ...    ...  ...    ...  \n",
       "6492     11.2   Legit      0    0      1  \n",
       "6493      9.6   Legit      0    0      1  \n",
       "6494      9.4   Legit      0    0      1  \n",
       "6495     12.8   Legit      0    0      1  \n",
       "6496     11.8   Legit      0    0      1  \n",
       "\n",
       "[6497 rows x 15 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Разделите данные на признаки X и целевую переменную y (колонка \"quality\")**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('quality', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['quality']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Разбейте данные на обучающий и тестовый набор данных, в отношении 90% и 10%. Замечание - в решении мы используем random state = 101**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Смасштабируйте данные в наборах X train и X test.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Создайте экземпляр классификатора Support Vector Machine. Раньше мы оставляли эту модель \"пустой\" (то есть, без параметров). Однако, поскольку мы знаем, что классы несбалансированы, для решения этой проблемы мы можем автоматически подобрать веса обратно пропорционально тому, как часто встречаются классы во входных переменных, используя параметр в вызове метода SVC(). Выясните в онлайн-документации по SVC, что это за параметр.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Напишите Ваш код здесь\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(class_weight='balanced')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: С помощью GridSearchCV выполните поиск по сетке значений параметров C и gamma.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Напишите Ваш код здесь\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 0.5, 0.75, 1], 'gamma': ['scale', 'auto']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(model, param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(class_weight=&#x27;balanced&#x27;),\n",
       "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 0.5, 0.75, 1],\n",
       "                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(class_weight=&#x27;balanced&#x27;),\n",
       "             param_grid={&#x27;C&#x27;: [0.001, 0.01, 0.1, 0.5, 0.75, 1],\n",
       "                         &#x27;gamma&#x27;: [&#x27;scale&#x27;, &#x27;auto&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVC(class_weight='balanced'),\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 0.5, 0.75, 1],\n",
       "                         'gamma': ['scale', 'auto']})"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.1, 'gamma': 'scale'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, class_weight=&#x27;balanced&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=0.1, class_weight='balanced')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ЗАДАНИЕ: Отобразите матрицу \"confusion matrix\" и отчёт \"classification report\" для Вашей модели.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = grid.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 27,   0],\n",
       "       [  0, 623]], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Fraud       1.00      1.00      1.00        27\n",
      "       Legit       1.00      1.00      1.00       623\n",
      "\n",
      "    accuracy                           1.00       650\n",
      "   macro avg       1.00      1.00      1.00       650\n",
      "weighted avg       1.00      1.00      1.00       650\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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