Email_Spam-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Email Spam Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import string \n",
    "%matplotlib inline\n",
    "import collections\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import feature_extraction\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from nltk.corpus import stopwords\n",
    "import nltk\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.gitignore',\n",
       " '.ipynb_checkpoints',\n",
       " 'ML-Pipeline',\n",
       " 'ML-Pipeline.ipynb',\n",
       " 'README.md',\n",
       " 'Spam.csv']"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/Users/KBrig/ML-Pipeline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part1: Data Reading and Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "path='/Users/KBrig/ML-Pipeline/spam.csv'\n",
    "#EmData= pd.read_csv('Spam.csv',encoding='cp1252')\n",
    "EmData= pd.read_csv(path,encoding='cp1252',dtype={'type':np.str, 'Email_body':np.str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "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>type</th>\n",
       "      <th>Email_body</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   type                                         Email_body Unnamed: 2  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EmData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "EmData=EmData[['type','Email_body']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column type : <class 'str'>\n",
      "column Email_body : <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "#print(EmData.shape)\n",
    "for col in EmData.columns:\n",
    "    print ('column', col,':', type(col[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 2 columns):\n",
      "type          5572 non-null object\n",
      "Email_body    5572 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.1+ KB\n"
     ]
    }
   ],
   "source": [
    "EmData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Email_body</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ham</th>\n",
       "      <td>4825</td>\n",
       "      <td>4516</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spam</th>\n",
       "      <td>747</td>\n",
       "      <td>653</td>\n",
       "      <td>Please call our customer service representativ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Email_body                                                               \n",
       "          count unique                                                top freq\n",
       "type                                                                          \n",
       "ham        4825   4516                             Sorry, I'll call later   30\n",
       "spam        747    653  Please call our customer service representativ...    4"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EmData.groupby('type').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16443c4b4e0>"
      ]
     },
     "execution_count": 307,
     "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(x='type',data=EmData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EmData['y']=EmData['type'].apply(lambda x: x =='spam').astype(int)\n",
    "EmData['type_num']= EmData.type.map({'ham':0, 'spam':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "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>type</th>\n",
       "      <th>Email_body</th>\n",
       "      <th>type_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   type                                         Email_body  type_num\n",
       "0   ham  Go until jurong point, crazy.. Available only ...         0\n",
       "1   ham                      Ok lar... Joking wif u oni...         0\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...         1\n",
       "3   ham  U dun say so early hor... U c already then say...         0\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...         0"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EmData.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# Part2: Data Splitting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5572,)\n",
      "(5572,)\n"
     ]
    }
   ],
   "source": [
    "X = EmData.Email_body\n",
    "y= EmData.type_num\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3900,)\n",
      "(1672,)\n",
      "(3900,)\n",
      "(1672,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Part3: Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Extraction with default values of \"CountVectorizer\" and \"English\" stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "def pip(classifier):\n",
    "    #SW = set(stopwords.words('english'))\n",
    "    pipeline = Pipeline([\n",
    "        ('vect', CountVectorizer()),  # strings to token integer counts\n",
    "        ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "        ('clf', classifier),\n",
    "    ])\n",
    "    return(pipeline)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "#tfidf = TfidfVectorizer(min_df=2, max_df=0.5, ngram_range=(2, 3))\n",
    "#features = tfidf.fit_transform(X_train)\n",
    "#pd.DataFrame(\n",
    " #   features.todense(),\n",
    "  #  columns=tfidf.get_feature_names())\n",
    "#tfidf.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Part4: Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Naieve Bayes Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "pip_NB= pip(MultinomialNB(fit_prior=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip...near_tf=False, use_idf=True)), ('clf', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=False))])"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_NB.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving NB model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model to disk\n",
    "filename = 'NaieveBayesClassifier.sav'\n",
    "pickle.dump(pip_NB, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Testing NB Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_NB = pip_NB.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.99      1471\n",
      "          1       0.87      0.94      0.90       201\n",
      "\n",
      "avg / total       0.98      0.98      0.98      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions_NB,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9293596910768144"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB_roc_auc_score=roc_auc_score(y_test,predictions_NB)\n",
    "NB_roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "pip_LR= pip(LogisticRegression(random_state=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip...nalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_LR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving LR model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'LogisticRegressionClassifier.sav'\n",
    "pickle.dump(pip_LR, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing LR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_LR = pip_LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Performance Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.97      0.98      1497\n",
      "          1       0.79      0.99      0.88       175\n",
      "\n",
      "avg / total       0.98      0.97      0.97      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions_LR,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8961012329164721"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ROC score\n",
    "LR_roc_auc_score=roc_auc_score(y_test,predictions_LR)\n",
    "LR_roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# SVM Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "pip_SVM= pip(SGDClassifier(random_state=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip...lty='l2', power_t=0.5, random_state=0, shuffle=True,\n",
       "       tol=None, verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_SVM.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'SVMclassifier.sav'\n",
    "pickle.dump(pip_SVM, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_SVM = pip_SVM.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.99      0.99      1470\n",
      "          1       0.91      0.99      0.95       202\n",
      "\n",
      "avg / total       0.99      0.99      0.99      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions_SVM,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9553903814847999"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM_roc_auc_score=roc_auc_score(y_test,predictions_SVM)\n",
    "SVM_roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Decision Trees Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "pip_DT=pip(tree.DecisionTreeClassifier())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip...      min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best'))])"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_DT.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'DecisionTreeClassifier.sav'\n",
    "pickle.dump(pip_DT, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_DT = pip_DT.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.98      0.98      1458\n",
      "          1       0.86      0.88      0.87       214\n",
      "\n",
      "avg / total       0.97      0.97      0.97      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions_DT,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9222518077306514"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT_roc_auc_score=roc_auc_score(y_test,predictions_DT)\n",
    "DT_roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Ensamble Methods Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "pip_EM = pip(RandomForestClassifier(n_estimators=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip...n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))])"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pip_EM.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'ENSampleMethodClassifier.sav'\n",
    "pickle.dump(pip_EM, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_EM = pip_EM.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.96      0.98      1507\n",
      "          1       0.76      1.00      0.86       165\n",
      "\n",
      "avg / total       0.98      0.97      0.97      1672\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predictions_EM,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8784403669724771"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EM_roc_auc_score=roc_auc_score(y_test,predictions_EM)\n",
    "EM_roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Comparing the accuracy of different ML models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9293596910768144,\n",
       " 0.8961012329164721,\n",
       " 0.9553903814847999,\n",
       " 0.9222518077306514,\n",
       " 0.8784403669724771]"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=[NB_roc_auc_score,LR_roc_auc_score,SVM_roc_auc_score,DT_roc_auc_score,EM_roc_auc_score]\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NB', 'LR', 'SVM', 'DT', 'EM']"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models= [\"NB\", \"LR\", \"SVM\", \"DT\", \"EM\"]\n",
    "models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'model': models,'accuracy': accuracy}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.929360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.896101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.955390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.922252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM</td>\n",
       "      <td>0.878440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  accuracy\n",
       "0    NB  0.929360\n",
       "1    LR  0.896101\n",
       "2   SVM  0.955390\n",
       "3    DT  0.922252\n",
       "4    EM  0.878440"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Performane = pd.DataFrame(data=d)\n",
    "Performane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Comparing the Accuracy of Different ML Models')"
      ]
     },
     "execution_count": 357,
     "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": [
    "ax=sns.boxplot(x='model', y='accuracy', data=Performane,linewidth=5)\n",
    "ax.set_title(\"Comparing the Accuracy of Different ML Models\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Model Tuning with Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(pip_SVM, X_train, y_train, scoring='accuracy', cv=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pip= [MultinomialNB(), LogisticRegression(random_state=0), SGDClassifier(random_state=0), tree.DecisionTreeClassifier(), RandomForestClassifier(n_estimators=10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9835900609422097"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.987962898088087, 0.9908732567098839, 0.991940947866372, 0.8899256605017761, 0.984305125204067]\n"
     ]
    }
   ],
   "source": [
    "Acc_with_CV=[]\n",
    "for l in model_pip:\n",
    "    Acc_with_CV.append (cross_val_score(pip(l), X_train, y_train, scoring='roc_auc', cv=5).mean())\n",
    "print(Acc_with_CV)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "Performane['Accuracy_with_CV']=Acc_with_CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>Accuracy_with_CV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NB</td>\n",
       "      <td>0.950005</td>\n",
       "      <td>0.987963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.879702</td>\n",
       "      <td>0.990873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.944266</td>\n",
       "      <td>0.991941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>0.907572</td>\n",
       "      <td>0.889926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EM</td>\n",
       "      <td>0.905276</td>\n",
       "      <td>0.984305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  accuracy  Accuracy_with_CV\n",
       "0    NB  0.950005          0.987963\n",
       "1    LR  0.879702          0.990873\n",
       "2   SVM  0.944266          0.991941\n",
       "3    DT  0.907572          0.889926\n",
       "4    EM  0.905276          0.984305"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Performane.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##STEMMING\n",
    "\n",
    "#from nltk.stem.snowball import SnowballStemmer\n",
    "#stemmer = SnowballStemmer(\"english\", ignore_stopwords=True)\n",
    "\n",
    "#class StemmedCountVectorizer(CountVectorizer):\n",
    "#    def build_analyzer(self):\n",
    "#        analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n",
    "#        return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])\n",
    "\n",
    "#stemmed_count_vect = StemmedCountVectorizer(stop_words='english')\n",
    "\n",
    "#text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect),\n",
    "#                            ('tfidf', TfidfTransformer()),\n",
    " #                            ('mnb', MultinomialNB()),\n",
    "  #                          ])\n",
    "\n",
    "#text_mnb_stemmed = text_mnb_stemmed.fit(X_train, y_train)\n",
    "\n",
    "#predicted_mnb_stemmed = text_mnb_stemmed.predict(X_test)\n",
    "\n",
    "#np.mean(predicted_mnb_stemmed == y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Parameters Tuning with GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing differnt parameters, e.g. comparing the performance of NB with unigram and Bigram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    " parameters_NB = {'vect__ngram_range': [(1, 2), (2, 3)],\n",
    "               'tfidf__use_idf': (True, False),\n",
    "               'clf__alpha': (1e-2, 1e-3),\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_clf_NB = GridSearchCV(pip(MultinomialNB(fit_prior=False)), parameters, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_clf_NB = gs_clf_NB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9828205128205129"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_clf_NB.best_score_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "{'clf__alpha': 0.01, 'tfidf__use_idf': False, 'vect__ngram_range': (1, 2)}"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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