Python/ml/E-Mail_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# E-MAIL CLASSIFICATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link to the Dataset: [E-Mails](https://www.kaggle.com/datatattle/email-classification-nlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\DELL\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import nltk \n",
    "nltk.download('stopwords')                 # download the stopwords from NLTK\n",
    "\n",
    "import re                                  # library for regular expression operations\n",
    "import string                              # for string operations\n",
    "\n",
    "from nltk.corpus import stopwords          # module for stop words that come with NLTK\n",
    "from nltk.stem import PorterStemmer        # module for stemming\n",
    "from nltk.tokenize import TweetTokenizer   # module for tokenizing strings\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression  \n",
    "from sklearn.feature_extraction.text import CountVectorizer  \n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt            # library for visualization\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S. No.</th>\n",
       "      <th>Message_body</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>953</td>\n",
       "      <td>hows my favourite person today? r u workin har...</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>954</td>\n",
       "      <td>How much you got for cleaning</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>955</td>\n",
       "      <td>Sorry da. I gone mad so many pending works wha...</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>956</td>\n",
       "      <td>Wat time ü finish?</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>957</td>\n",
       "      <td>Just glad to be talking to you.</td>\n",
       "      <td>Non-Spam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>957 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     S. No.                                       Message_body     Label\n",
       "0         1                         Rofl. Its true to its name  Non-Spam\n",
       "1         2  The guy did some bitching but I acted like i'd...  Non-Spam\n",
       "2         3  Pity, * was in mood for that. So...any other s...  Non-Spam\n",
       "3         4               Will ü b going to esplanade fr home?  Non-Spam\n",
       "4         5  This is the 2nd time we have tried 2 contact u...      Spam\n",
       "..      ...                                                ...       ...\n",
       "952     953  hows my favourite person today? r u workin har...  Non-Spam\n",
       "953     954                      How much you got for cleaning  Non-Spam\n",
       "954     955  Sorry da. I gone mad so many pending works wha...  Non-Spam\n",
       "955     956                                 Wat time ü finish?  Non-Spam\n",
       "956     957                    Just glad to be talking to you.  Non-Spam\n",
       "\n",
       "[957 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'email.csv', encoding = 'unicode_escape')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Message_body</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>hows my favourite person today? r u workin har...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>How much you got for cleaning</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>Sorry da. I gone mad so many pending works wha...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>Wat time ü finish?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>Just glad to be talking to you.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>957 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Message_body  Label\n",
       "0                           Rofl. Its true to its name      0\n",
       "1    The guy did some bitching but I acted like i'd...      0\n",
       "2    Pity, * was in mood for that. So...any other s...      0\n",
       "3                 Will ü b going to esplanade fr home?      0\n",
       "4    This is the 2nd time we have tried 2 contact u...      1\n",
       "..                                                 ...    ...\n",
       "952  hows my favourite person today? r u workin har...      0\n",
       "953                      How much you got for cleaning      0\n",
       "954  Sorry da. I gone mad so many pending works wha...      0\n",
       "955                                 Wat time ü finish?      0\n",
       "956                    Just glad to be talking to you.      0\n",
       "\n",
       "[957 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['S. No.'], axis=1)  # dropping unnecesary column\n",
    "label_encoder = preprocessing.LabelEncoder()  # label encoding for 'Label' column\n",
    "df['Label'] = label_encoder.fit_transform(df['Label'])   # label encoding column - MSZoning for an example \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Message_body    False\n",
       "Label           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()  # checking for null values if any"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing steps in NLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_mail(mail):\n",
    "    \"\"\"Process mail function.\n",
    "    Input:\n",
    "        mail: a string containing meassage body\n",
    "    Output:\n",
    "        mail_clean: a list of words containing the processed body\n",
    "\n",
    "    \"\"\"\n",
    "    stemmer = PorterStemmer()\n",
    "    stopwords_english = stopwords.words('english')\n",
    "    # tokenize reviews\n",
    "    tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True,\n",
    "                               reduce_len=True)\n",
    "    mail_tokens = tokenizer.tokenize(mail)\n",
    "\n",
    "    mail_clean = []\n",
    "    for word in mail_tokens:\n",
    "        if (word not in stopwords_english and  # remove stopwords\n",
    "                word not in string.punctuation):  # remove punctuation\n",
    "            # mail_clean.append(word)\n",
    "            stem_word = stemmer.stem(word)  # stemming word\n",
    "            mail_clean.append(stem_word)\n",
    "\n",
    "    return mail_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Message_body</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[rofl, true, name]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[guy, bitch, act, like, i'd, interest, buy, so...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[piti, mood, ..., suggest]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[ü, b, go, esplanad, fr, home]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[2nd, time, tri, 2, contact, u, u, £, 750, pou...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>[how, favourit, person, today, r, u, workin, h...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>[much, got, clean]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>[sorri, da, gone, mad, mani, pend, work]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>[wat, time, ü, finish]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>[glad, talk]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>957 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Message_body  Label\n",
       "0                                   [rofl, true, name]      0\n",
       "1    [guy, bitch, act, like, i'd, interest, buy, so...      0\n",
       "2                           [piti, mood, ..., suggest]      0\n",
       "3                       [ü, b, go, esplanad, fr, home]      0\n",
       "4    [2nd, time, tri, 2, contact, u, u, £, 750, pou...      1\n",
       "..                                                 ...    ...\n",
       "952  [how, favourit, person, today, r, u, workin, h...      0\n",
       "953                                 [much, got, clean]      0\n",
       "954           [sorri, da, gone, mad, mani, pend, work]      0\n",
       "955                             [wat, time, ü, finish]      0\n",
       "956                                       [glad, talk]      0\n",
       "\n",
       "[957 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the process_mail function for:\n",
    "# 1. Removing stop words\n",
    "# 2. Tokenization\n",
    "# 3. Stemming\n",
    "A = []\n",
    "a = df['Message_body']\n",
    "for i in a:\n",
    "  i = process_mail(i)\n",
    "  A.append(i)\n",
    "df['Message_body'] = A\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer(max_features=1500, analyzer='word', lowercase=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Message_body'] = df['Message_body'].apply(lambda x: \" \".join(x) )  # to join all words in the lists\n",
    "X = cv.fit_transform(df['Message_body'])  # predictor variable 'X'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>piti mood ... suggest</td>\n",
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       "      <th>3</th>\n",
       "      <td>ü b go esplanad fr home</td>\n",
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       "      <th>4</th>\n",
       "      <td>2nd time tri 2 contact u u £ 750 pound prize 2...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>952</th>\n",
       "      <td>how favourit person today r u workin hard slee...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>much got clean</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>sorri da gone mad mani pend work</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>wat time ü finish</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>glad talk</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>957 rows × 2 columns</p>\n",
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      "text/plain": [
       "                                          Message_body  Label\n",
       "0                                       rofl true name      0\n",
       "1    guy bitch act like i'd interest buy someth els...      0\n",
       "2                                piti mood ... suggest      0\n",
       "3                              ü b go esplanad fr home      0\n",
       "4    2nd time tri 2 contact u u £ 750 pound prize 2...      1\n",
       "..                                                 ...    ...\n",
       "952  how favourit person today r u workin hard slee...      0\n",
       "953                                     much got clean      0\n",
       "954                   sorri da gone mad mani pend work      0\n",
       "955                                  wat time ü finish      0\n",
       "956                                          glad talk      0\n",
       "\n",
       "[957 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<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>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Label\n",
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = pd.DataFrame(df['Label'])  # respose variable 'y'\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 0)  # splitting in the ratio 80:20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7833333333333333"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions are 78.33% accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results' Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[162,   0],\n",
       "       [ 13,  17]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 0.7833333333333333')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.heatmap(cm, annot=True, fmt=\".0f\", linewidths=0.5, square = True, cmap = 'Pastel1')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(roc_auc_score(y_test, y_pred))\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}