{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='https://www.udemy.com/user/joseportilla/'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines \n",
    "## Exercise - Solutions\n",
    "\n",
    "## Fraud in Wine\n",
    "\n",
    "Wine fraud relates to the commercial aspects of wine. The most prevalent type of fraud is one where wines are adulterated, usually with the addition of cheaper products (e.g. juices) and sometimes with harmful chemicals and sweeteners (compensating for color or flavor).\n",
    "\n",
    "Counterfeiting and the relabelling of inferior and cheaper wines to more expensive brands is another common type of wine fraud.\n",
    "\n",
    "<img src=\"wine.jpg\">\n",
    "\n",
    "## Project Goals\n",
    "\n",
    "A distribution company that was recently a victim of fraud has completed an audit of various samples of wine through the use of chemical analysis on samples. The distribution company specializes in exporting extremely high quality, expensive wines, but was defrauded by a supplier who was attempting to pass off cheap, low quality wine as higher grade wine. The distribution company has hired you to attempt to create a machine learning model that can help detect low quality (a.k.a \"fraud\") wine samples. They want to know if it is even possible to detect such a difference.\n",
    "\n",
    "\n",
    "Data Source: *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",
    "**TASK: Your overall goal is to use the wine dataset shown below to develop a machine learning model that attempts to predict if a wine is \"Legit\" or \"Fraud\" based on various chemical features. Complete the tasks below to follow along with the project.**\n",
    "\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Complete the Tasks in bold\n",
    "\n",
    "**TASK: Run the cells below to import the libraries and load the dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Git-Github\\DATA\\wine_fraud.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: What are the unique variables in the target column we are trying to predict (quality)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Legit', 'Fraud'], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a countplot that displays the count per category of Legit vs Fraud. Is the label/target balanced or unbalanced?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Legit    6251\n",
       "Fraud     246\n",
       "Name: quality, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['quality'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='quality', ylabel='count'>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='quality',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Let's find out if there is a difference between red and white wine when it comes to fraud. Create a countplot that has the wine *type* on the x axis with the hue separating columns by Fraud vs Legit.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type', ylabel='count'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='type',hue='quality',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: What percentage of red wines are Fraud? What percentage of white wines are fraud?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "reds = df[df[\"type\"]=='red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "whites = df[df[\"type\"]=='white']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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(100* (len(reds[reds['quality']=='Fraud'])/len(reds)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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(100* (len(whites[whites['quality']=='Fraud'])/len(whites)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Calculate the correlation between the various features and the \"quality\" column. To do this you may need to map the column to 0 and 1 instead of a string.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fraud']= df['quality'].map({'Legit':0,'Fraud':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Fraud']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a bar plot of the correlation values to Fraudlent wine.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr()['Fraud'][:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create a clustermap with seaborn to explore the relationships between variables.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x1889caf9370>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df.corr(),annot=True,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## Machine Learning Model\n",
    "\n",
    "**TASK: Convert the categorical column \"type\" from a string or \"red\" or \"white\" to dummy variables:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['type'] = pd.get_dummies(df['type'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('Fraud',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Separate out the data into X features and y target label (\"quality\" column)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('quality',axis=1)\n",
    "y = df['quality']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Perform a Train|Test split on the data, with a 10% test size. Note: The solution uses a random state of 101**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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": [
    "**TASK: Scale the X train and X test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Create an instance of a Support Vector Machine classifier. Previously we have left this model \"blank\", (e.g. with no parameters). However, we already know that the classes are unbalanced, in an attempt to help alleviate this issue, we can automatically adjust weights inversely proportional to class frequencies in the input data with a argument call in the SVC() call. Check out the [documentation for SVC] online and look up what the argument\\parameter is.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(class_weight='balanced')\n",
    "\n",
    "# the balanced weight gives ,ore weight in the training process to less represented data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Use a GridSearchCV to run a grid search for the best C and gamma parameters.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C':[0.001,0.01,0.1,0.5,1],'gamma':['scale','auto']}\n",
    "grid = GridSearchCV(svc,param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(class_weight='balanced'),\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 0.5, 1],\n",
       "                         'gamma': ['scale', 'auto']})"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(scaled_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'gamma': 'auto'}"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Display the confusion matrix and classification report for your model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_pred = grid.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 17,  10],\n",
       "       [ 92, 531]], dtype=int64)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,grid_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x18893288d30>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(grid, scaled_X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       Fraud       0.16      0.63      0.25        27\n",
      "       Legit       0.98      0.85      0.91       623\n",
      "\n",
      "    accuracy                           0.84       650\n",
      "   macro avg       0.57      0.74      0.58       650\n",
      "weighted avg       0.95      0.84      0.88       650\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,grid_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TASK: Finally, think about how well this model performed, would you suggest using it? Realistically will this work?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View video for full discussion on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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