3. Text-classification/Spam detection.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Spam detection using Random mForest"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.pipeline import Pipeline"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load the data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                text label\n0  Go until jurong point, crazy.. Available only ...   ham\n1                      Ok lar... Joking wif u oni...   ham\n2  Free entry in 2 a wkly comp to win FA Cup fina...  spam\n3  U dun say so early hor... U c already then say...   ham\n4  Nah I don't think he goes to usf, he lives aro...   ham",
      "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>text</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Go until jurong point, crazy.. Available only ...</td>\n      <td>ham</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ok lar... Joking wif u oni...</td>\n      <td>ham</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n      <td>spam</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>U dun say so early hor... U c already then say...</td>\n      <td>ham</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Nah I don't think he goes to usf, he lives aro...</td>\n      <td>ham</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sms_spam.csv', encoding_errors='ignore')\n",
    "df = df[['v2', 'v1']]\n",
    "df.rename(columns={\"v2\": \"text\", \"v1\": \"label\"}, inplace=True)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Split the dataset into training and testing sets"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df['text'],\n",
    "    df['label'],\n",
    "    test_size=0.2,\n",
    "    random_state=42\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## TF-IDF"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### TF-IDF only"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Initialize the TF-IDF vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit and transform the training data\n",
    "train_vectors = tfidf_vectorizer.fit_transform(train_data)\n",
    "\n",
    "# Transform the testing data\n",
    "test_vectors = tfidf_vectorizer.transform(test_data)\n",
    "\"\"\""
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## TF-IDF + CountVectorizer\n",
    "It is common to combine TF-IF and Count vectorizer."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer()\n",
    "\n",
    "X_train_vectorized = vectorizer.fit_transform(X_train)\n",
    "X_train_vectorized.shape\n",
    "\n",
    "X_test_vectorized = vectorizer.transform(X_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "(4457, 7704)"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_train_vectorized.shape"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Training the model"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "RandomForestClassifier()",
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     },
     "execution_count": 39,
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   ],
   "source": [
    "# Create a Random Forest classifier\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "# Train the classifier on the TF-IDF transformed training data and corresponding labels\n",
    "clf.fit(X_train_vectorized, y_train)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9766816143497757\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test_vectorized)\n",
    "# Calculate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Building a pipeline\n",
    "Instead of performng Vectorization and Training separately, we can combine them in a pipeline."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "clf_pipeline = Pipeline([('tfidf', TfidfVectorizer()),\n",
    "                     ('clf', RandomForestClassifier()),\n",
    "])\n",
    "\n",
    "# X_train will be vectorized before training\n",
    "clf_pipeline.fit(X_train, y_train)\n",
    "\n",
    "# predict\n",
    "y_pred = clf_pipelinelf_pipeline.predict(X_test)"
   ],
   "metadata": {
    "collapsed": false
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  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.97      1.00      0.99       965\n",
      "        spam       1.00      0.81      0.90       150\n",
      "\n",
      "    accuracy                           0.97      1115\n",
      "   macro avg       0.99      0.91      0.94      1115\n",
      "weighted avg       0.98      0.97      0.97      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ],
   "metadata": {
    "collapsed": false
   }
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