nb1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20800, 5)\n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>Darrell Lucus</td>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>FLYNN: Hillary Clinton, Big Woman on Campus - ...</td>\n",
       "      <td>Daniel J. Flynn</td>\n",
       "      <td>Ever get the feeling your life circles the rou...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Why the Truth Might Get You Fired</td>\n",
       "      <td>Consortiumnews.com</td>\n",
       "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15 Civilians Killed In Single US Airstrike Hav...</td>\n",
       "      <td>Jessica Purkiss</td>\n",
       "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
       "      <td>Howard Portnoy</td>\n",
       "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                              title              author  \\\n",
       "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...       Darrell Lucus   \n",
       "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...     Daniel J. Flynn   \n",
       "2   2                  Why the Truth Might Get You Fired  Consortiumnews.com   \n",
       "3   3  15 Civilians Killed In Single US Airstrike Hav...     Jessica Purkiss   \n",
       "4   4  Iranian woman jailed for fictional unpublished...      Howard Portnoy   \n",
       "\n",
       "                                                text  label  \n",
       "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
       "1  Ever get the feeling your life circles the rou...      0  \n",
       "2  Why the Truth Might Get You Fired October 29, ...      1  \n",
       "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18285, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.dropna()\n",
    "df1.reset_index(drop=True)\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1.iloc[:,:-1]\n",
    "y = df1.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label\n",
      "0    10361\n",
      "1     7924\n",
      "Name: count, dtype: int64\n",
      "0: Reliable\n",
      "1: Unreliable\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_label_counts = y.value_counts()\n",
    "print(class_label_counts)\n",
    "print(\"0: Reliable\\n1: Unreliable\")\n",
    "plt.bar(['Reliable', 'Unreliable'], class_label_counts.values, color=['blue', 'orange'])\n",
    "\n",
    "plt.xlabel('Class Label')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Class Label Frequency Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18285, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stemming and removing Stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "ps = PorterStemmer()\n",
    "corpus = []\n",
    "for i in range(len(x)):\n",
    "    review = re.sub('[^A-Za-z]',' ',x.iloc[i,1]) # title\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [ps.stem(word) for word in review if word not in set(stopwords.words('english'))]\n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hous dem aid even see comey letter jason chaffetz tweet'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "# Bag of Words\n",
    "cv = CountVectorizer(ngram_range=(1,3), max_features=5000)\n",
    "X = cv.fit_transform(corpus).toarray()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['abandon', 'abc', 'abc news', 'abduct', 'abe', 'abedin', 'abl',\n",
       "       'abort', 'abroad', 'absolut'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.get_feature_names_out()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, classification_report, accuracy_score\n",
    "\n",
    "mnb = MultinomialNB()\n",
    "mnb.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Reliable       0.92      0.89      0.90      2600\n",
      "  Unreliable       0.86      0.90      0.88      1972\n",
      "\n",
      "    accuracy                           0.89      4572\n",
      "   macro avg       0.89      0.89      0.89      4572\n",
      "weighted avg       0.89      0.89      0.89      4572\n",
      "\n",
      "Accuracy: 0.8904199475065617\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = mnb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=['Reliable', 'Unreliable']))\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "ConfusionMatrixDisplay.from_predictions( y_test, y_pred, display_labels=['Reliable', 'Unreliable'])\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score:  0.9040320922988642\n",
      "Best Parameters:  {'alpha': 1.1}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "parameters = {  \n",
    "'alpha': ( 1.2, 1.15 , 1.1, 1.05, 1, 0.1, 0.01, 0.001)  \n",
    "}\n",
    "grid = GridSearchCV(mnb, parameters, cv=StratifiedKFold(5).split(X_train, y_train)) # 5 fold Stratified Cross Validation\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "print('Training Score: ',grid.best_score_)\n",
    "print(\"Best Parameters: \", grid.best_params_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Reliable       0.92      0.88      0.90      2600\n",
      "  Unreliable       0.85      0.90      0.88      1972\n",
      "\n",
      "    accuracy                           0.89      4572\n",
      "   macro avg       0.89      0.89      0.89      4572\n",
      "weighted avg       0.89      0.89      0.89      4572\n",
      "\n",
      "Accuracy: 0.889763779527559\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = grid.best_estimator_.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=['Reliable', 'Unreliable']))\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "ConfusionMatrixDisplay.from_predictions( y_test, y_pred, display_labels=['Reliable', 'Unreliable'])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "/home/anshuman/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score:  0.9347325584147221\n",
      "Best Parameters:  {'C': 1, 'penalty': 'l2', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "lr = LogisticRegression()\n",
    "param_grid = {\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "    'solver': ['liblinear', 'saga']\n",
    "}\n",
    "grid_search = GridSearchCV(estimator=lr, param_grid=param_grid, cv=StratifiedKFold(5).split(X_train, y_train), scoring='accuracy')\n",
    "\n",
    "grid_search.fit(X_train, y_train)\n",
    "print('Training Score: ',grid_search.best_score_)\n",
    "\n",
    "print(\"Best Parameters: \", grid_search.best_params_)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Reliable       0.97      0.89      0.93      2600\n",
      "  Unreliable       0.87      0.97      0.92      1972\n",
      "\n",
      "    accuracy                           0.93      4572\n",
      "   macro avg       0.92      0.93      0.92      4572\n",
      "weighted avg       0.93      0.93      0.93      4572\n",
      "\n",
      "Accuracy: 0.9251968503937008\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = grid_search.best_estimator_.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=['Reliable', 'Unreliable']))\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "ConfusionMatrixDisplay.from_predictions( y_test, y_pred, display_labels=['Reliable', 'Unreliable'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implemeneting TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(ngram_range=(1,3), max_features=5000)\n",
    "X1 = tfidf.fit_transform(corpus).toarray()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression on TFIDF vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Reliable       0.98      0.87      0.92      2600\n",
      "  Unreliable       0.85      0.98      0.91      1972\n",
      "\n",
      "    accuracy                           0.92      4572\n",
      "   macro avg       0.92      0.92      0.92      4572\n",
      "weighted avg       0.92      0.92      0.92      4572\n",
      "\n",
      "Accuracy: 0.915573053368329\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=['Reliable', 'Unreliable']))\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "ConfusionMatrixDisplay.from_predictions( y_test, y_pred, display_labels=['Reliable', 'Unreliable'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naive Bayes on Tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Reliable       0.86      0.94      0.90      2600\n",
      "  Unreliable       0.91      0.80      0.85      1972\n",
      "\n",
      "    accuracy                           0.88      4572\n",
      "   macro avg       0.89      0.87      0.88      4572\n",
      "weighted avg       0.88      0.88      0.88      4572\n",
      "\n",
      "Accuracy: 0.8803587051618548\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnb = MultinomialNB()\n",
    "mnb.fit(X_train,y_train)\n",
    "y_pred = mnb.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=['Reliable', 'Unreliable']))\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n",
    "ConfusionMatrixDisplay.from_predictions( y_test, y_pred, display_labels=['Reliable', 'Unreliable'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}