supervised/support vector machine.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vector machine project\n",
    "##### For the course 2022 Python Machine Learning & Data Science Masterclass\n",
    "\n",
    "For this project, we will use the [Wine fraud dataset](https://www.sciencedirect.com/science/article/abs/pii/S0167923609001377) and create a SVM model capable of detecting low quality (or \"fraud\") wine samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration\n",
    "\n",
    "We will first check the wine characteristics that will help us identify the quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df = pd.read_csv('data/Wine_fraud.csv')\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6497 entries, 0 to 6496\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   fixed acidity         6497 non-null   float64\n",
      " 1   volatile acidity      6497 non-null   float64\n",
      " 2   citric acid           6497 non-null   float64\n",
      " 3   residual sugar        6497 non-null   float64\n",
      " 4   chlorides             6497 non-null   float64\n",
      " 5   free sulfur dioxide   6497 non-null   float64\n",
      " 6   total sulfur dioxide  6497 non-null   float64\n",
      " 7   density               6497 non-null   float64\n",
      " 8   pH                    6497 non-null   float64\n",
      " 9   sulphates             6497 non-null   float64\n",
      " 10  alcohol               6497 non-null   float64\n",
      " 11  quality               6497 non-null   object \n",
      " 12  type                  6497 non-null   object \n",
      "dtypes: float64(11), object(2)\n",
      "memory usage: 660.0+ KB\n"
     ]
    }
   ],
   "source": [
    "wine_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will check for the categorical columns first. \n",
    "\n",
    "We know that we have a \"quality\" column, let's see its possible values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Legit    6251\n",
       "Fraud     246\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df[\"quality\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the \"type\" feature? Let's check the possible values and the relationship with the quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white    4898\n",
       "red      1599\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df[\"type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f881a719a00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=wine_df, x='type', hue='quality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_wine = wine_df[wine_df[\"type\"] == \"red\"]\n",
    "white_wine = wine_df[wine_df[\"type\"] == \"white\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of fraud in Red Wines:\n",
      "3.9399624765478425\n",
      "\n",
      "\n",
      "Percentage of fraud in White Wines:\n",
      "3.7362188648427925\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of fraud in Red Wines:\")\n",
    "print(100* (len(red_wine[red_wine[\"quality\"] == \"Fraud\"]) / len(red_wine)))\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Percentage of fraud in White Wines:\")\n",
    "print(100* (len(white_wine[white_wine[\"quality\"] == \"Fraud\"]) / len(white_wine)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should change the \"quality\" column to an int column for the calculation of correlation between features. We can change the \"Legit\" value with a 0 and the \"Fraud\" value with a 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_df[\"Fraud\"] = wine_df[\"quality\"].map({\"Legit\": 0, \"Fraud\": 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can calculate the correlation between the numerical features and the Fraud column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.corr()['Fraud']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f881a89d130>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot how correlated to Fraud are the different features\n",
    "\n",
    "# the :-1 is for except the fraud column\n",
    "wine_df.corr()['Fraud'][:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7f881a9b2e80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize the correlation between features in a clustermap\n",
    "sns.clustermap(wine_df.corr(), cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f881ad53c10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(wine_df.corr(), cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creation of train and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we don't want duplicate columns, we need to remove the \"Fraud\" column. We already have \"quality\" as the predicion value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_df = wine_df.drop(\"Fraud\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also change the categorical \"type\" column to numerical using dummy variables. Since \"type\" has only two possible values, we can use the same column and have the values 0 and 1 instead of \"red\" and \"white\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_df[\"type\"] = pd.get_dummies(wine_df[\"type\"], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>0.99780</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <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>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>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>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>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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 13 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  type  \n",
       "0         9.4   Legit     0  \n",
       "1         9.8   Legit     0  \n",
       "2         9.8   Legit     0  \n",
       "3         9.8   Legit     0  \n",
       "4         9.4   Legit     0  \n",
       "...       ...     ...   ...  \n",
       "6492     11.2   Legit     1  \n",
       "6493      9.6   Legit     1  \n",
       "6494      9.4   Legit     1  \n",
       "6495     12.8   Legit     1  \n",
       "6496     11.8   Legit     1  \n",
       "\n",
       "[6497 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's prepare the features and the prediction, and then we can make the split for training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine_df.drop(\"quality\", axis=1)\n",
    "y = wine_df[\"quality\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=52)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model creation\n",
    "\n",
    "We will first create a support vector classifier and supply the value \"balanced\" to the \"class_weight\" parameter since we know our classes are unbalanced. This parameter defines the weight to apply to the C value in the calculation of the soft margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "smc_model = SVC(class_weight=\"balanced\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use GridSearchCV to find the best parameters to C and gamma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight='balanced', coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 0.5, 1],\n",
       "                         'gamma': ['scale', 'auto']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters_grid = {'C': [0.001, 0.01, 0.1, 0.5, 1],\n",
    "                   'gamma': ['scale', 'auto']}\n",
    "\n",
    "grid_model = GridSearchCV(smc_model, parameters_grid)\n",
    "grid_model.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 'auto'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the best parameters found\n",
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report\n",
    "\n",
    "grid_predictions = grid_model.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 21,  14],\n",
       "       [122, 818]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, grid_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f881b8229a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid_model, scaled_X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Fraud       0.15      0.60      0.24        35\n",
      "       Legit       0.98      0.87      0.92       940\n",
      "\n",
      "    accuracy                           0.86       975\n",
      "   macro avg       0.57      0.74      0.58       975\n",
      "weighted avg       0.95      0.86      0.90       975\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_predictions))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}