diff --git "a/MLOps NLP.ipynb" "b/MLOps NLP.ipynb"
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+++ "b/MLOps NLP.ipynb"
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***Importing Libraries***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import preprocess_kgptalkie as ps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>One of the other reviewers has mentioned that ...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Basically there's a family where a little boy ...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49995</th>\n",
+       "      <td>I thought this movie did a down right good job...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49996</th>\n",
+       "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49997</th>\n",
+       "      <td>I am a Catholic taught in parochial elementary...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49998</th>\n",
+       "      <td>I'm going to have to disagree with the previou...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49999</th>\n",
+       "      <td>No one expects the Star Trek movies to be high...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50000 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  review sentiment\n",
+       "0      One of the other reviewers has mentioned that ...  positive\n",
+       "1      A wonderful little production. <br /><br />The...  positive\n",
+       "2      I thought this was a wonderful way to spend ti...  positive\n",
+       "3      Basically there's a family where a little boy ...  negative\n",
+       "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
+       "...                                                  ...       ...\n",
+       "49995  I thought this movie did a down right good job...  positive\n",
+       "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
+       "49997  I am a Catholic taught in parochial elementary...  negative\n",
+       "49998  I'm going to have to disagree with the previou...  negative\n",
+       "49999  No one expects the Star Trek movies to be high...  negative\n",
+       "\n",
+       "[50000 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('C:/Users/user/Desktop/IMDB Dataset.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(50000, 2)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['review', 'sentiment'], dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>One of the other reviewers has mentioned that ...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Basically there's a family where a little boy ...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment\n",
+       "0  One of the other reviewers has mentioned that ...  positive\n",
+       "1  A wonderful little production. <br /><br />The...  positive\n",
+       "2  I thought this was a wonderful way to spend ti...  positive\n",
+       "3  Basically there's a family where a little boy ...  negative\n",
+       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\user\\anaconda\\Lib\\site-packages\\preprocess_kgptalkie\\utils.py:97: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n",
+      "  return BeautifulSoup(x, 'lxml').get_text().strip()\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['word_counts'] = df['review'].apply(lambda x: ps.get_wordcounts(x))\n",
+    "df['char_counts'] = df['review'].apply(lambda x: ps.get_charcounts(x))\n",
+    "df['avg_wordlength'] = df['review'].apply(lambda x: ps.get_avg_wordlength(x))\n",
+    "df['stopwords_counts'] = df['review'].apply(lambda x: ps.get_stopwords_counts(x))\n",
+    "\n",
+    "df['review'] = df['review'].apply(lambda x: ps.remove_html_tags(x))\n",
+    "df['review'] = df['review'].str.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['review_without_stopwords'] = df['review'].apply(lambda x: ps.remove_stopwords(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "      <th>word_counts</th>\n",
+       "      <th>char_counts</th>\n",
+       "      <th>avg_wordlength</th>\n",
+       "      <th>stopwords_counts</th>\n",
+       "      <th>review_without_stopwords</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>one of the other reviewers has mentioned that ...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>307</td>\n",
+       "      <td>1455</td>\n",
+       "      <td>4.739414</td>\n",
+       "      <td>135</td>\n",
+       "      <td>reviewers mentioned watching 1 oz episode you'...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>a wonderful little production. the filming tec...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>162</td>\n",
+       "      <td>837</td>\n",
+       "      <td>5.166667</td>\n",
+       "      <td>71</td>\n",
+       "      <td>wonderful little production. filming technique...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>i thought this was a wonderful way to spend ti...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>166</td>\n",
+       "      <td>761</td>\n",
+       "      <td>4.584337</td>\n",
+       "      <td>81</td>\n",
+       "      <td>thought wonderful way spend time hot summer we...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>basically there's a family where a little boy ...</td>\n",
+       "      <td>negative</td>\n",
+       "      <td>138</td>\n",
+       "      <td>611</td>\n",
+       "      <td>4.427536</td>\n",
+       "      <td>63</td>\n",
+       "      <td>basically there's family little boy (jake) thi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>petter mattei's \"love in the time of money\" is...</td>\n",
+       "      <td>positive</td>\n",
+       "      <td>230</td>\n",
+       "      <td>1088</td>\n",
+       "      <td>4.730435</td>\n",
+       "      <td>107</td>\n",
+       "      <td>petter mattei's \"love time money\" visually stu...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment  word_counts  \\\n",
+       "0  one of the other reviewers has mentioned that ...  positive          307   \n",
+       "1  a wonderful little production. the filming tec...  positive          162   \n",
+       "2  i thought this was a wonderful way to spend ti...  positive          166   \n",
+       "3  basically there's a family where a little boy ...  negative          138   \n",
+       "4  petter mattei's \"love in the time of money\" is...  positive          230   \n",
+       "\n",
+       "   char_counts  avg_wordlength  stopwords_counts  \\\n",
+       "0         1455        4.739414               135   \n",
+       "1          837        5.166667                71   \n",
+       "2          761        4.584337                81   \n",
+       "3          611        4.427536                63   \n",
+       "4         1088        4.730435               107   \n",
+       "\n",
+       "                            review_without_stopwords  \n",
+       "0  reviewers mentioned watching 1 oz episode you'...  \n",
+       "1  wonderful little production. filming technique...  \n",
+       "2  thought wonderful way spend time hot summer we...  \n",
+       "3  basically there's family little boy (jake) thi...  \n",
+       "4  petter mattei's \"love time money\" visually stu...  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**STOP WORDS**\n",
+    "```\n",
+    "{'call', 'upon', 'still', 'nevertheless', 'down', 'every', 'forty', '‘re', 'always', 'whole', 'side', \"n't\", 'now', 'however', 'an', 'show', 'least', 'give', 'below', 'did', 'sometimes', 'which', \"'s\", 'nowhere', 'per', 'hereupon', 'yours', 'she', 'moreover', 'eight', 'somewhere', 'within', 'whereby', 'few', 'has', 'so', 'have', 'for', 'noone', 'top', 'were', 'those', 'thence', 'eleven', 'after', 'no', '’ll', 'others', 'ourselves', 'themselves', 'though', 'that', 'nor', 'just', '’s', 'before', 'had', 'toward', 'another', 'should', 'herself', 'and', 'these', 'such', 'elsewhere', 'further', 'next', 'indeed', 'bottom', 'anyone', 'his', 'each', 'then', 'both', 'became', 'third', 'whom', '‘ve', 'mine', 'take', 'many', 'anywhere', 'to', 'well', 'thereafter', 'besides', 'almost', 'front', 'fifteen', 'towards', 'none', 'be', 'herein', 'two', 'using', 'whatever', 'please', 'perhaps', 'full', 'ca', 'we', 'latterly', 'here', 'therefore', 'us', 'how', 'was', 'made', 'the', 'or', 'may', '’re', 'namely', \"'ve\", 'anyway', 'amongst', 'used', 'ever', 'of', 'there', 'than', 'why', 'really', 'whither', 'in', 'only', 'wherein', 'last', 'under', 'own', 'therein', 'go', 'seems', '‘m', 'wherever', 'either', 'someone', 'up', 'doing', 'on', 'rather', 'ours', 'again', 'same', 'over', '‘s', 'latter', 'during', 'done', \"'re\", 'put', \"'m\", 'much', 'neither', 'among', 'seemed', 'into', 'once', 'my', 'otherwise', 'part', 'everywhere', 'never', 'myself', 'must', 'will', 'am', 'can', 'else', 'although', 'as', 'beyond', 'are', 'too', 'becomes', 'does', 'a', 'everyone', 'but', 'some', 'regarding', '‘ll', 'against', 'throughout', 'yourselves', 'him', \"'d\", 'it', 'himself', 'whether', 'move', '’m', 'hereafter', 're', 'while', 'whoever', 'your', 'first', 'amount', 'twelve', 'serious', 'other', 'any', 'off', 'seeming', 'four', 'itself', 'nothing', 'beforehand', 'make', 'out', 'very', 'already', 'various', 'until', 'hers', 'they', 'not', 'them', 'where', 'would', 'since', 'everything', 'at', 'together', 'yet', 'more', 'six', 'back', 'with', 'thereupon', 'becoming', 'around', 'due', 'keep', 'somehow', 'n‘t', 'across', 'all', 'when', 'i', 'empty', 'nine', 'five', 'get', 'see', 'been', 'name', 'between', 'hence', 'ten', 'several', 'from', 'whereupon', 'through', 'hereby', \"'ll\", 'alone', 'something', 'formerly', 'without', 'above', 'onto', 'except', 'enough', 'become', 'behind', '’d', 'its', 'most', 'n’t', 'might', 'whereas', 'anything', 'if', 'her', 'via', 'fifty', 'is', 'thereby', 'twenty', 'often', 'whereafter', 'their', 'also', 'anyhow', 'cannot', 'our', 'could', 'because', 'who', 'beside', 'by', 'whence', 'being', 'meanwhile', 'this', 'afterwards', 'whenever', 'mostly', 'what', 'one', 'nobody', 'seem', 'less', 'do', '‘d', 'say', 'thus', 'unless', 'along', 'yourself', 'former', 'thru', 'he', 'hundred', 'three', 'sixty', 'me', 'sometime', 'whose', 'you', 'quite', '’ve', 'about', 'even'}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['review', 'sentiment', 'word_counts', 'char_counts', 'avg_wordlength',\n",
+       "       'stopwords_counts', 'review_without_stopwords'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,8))\n",
+    "\n",
+    "plt.subplot(2,2,1)\n",
+    "# sns.barplot(x='sentiment', y='word_counts', data=df)\n",
+    "sns.histplot(data=df, x='word_counts', hue='sentiment', kde=True)\n",
+    "plt.title('Word counts')\n",
+    "\n",
+    "plt.subplot(2,2,2)\n",
+    "sns.histplot(data=df, x='char_counts', hue='sentiment', kde=True)\n",
+    "plt.title('Char counts')\n",
+    "\n",
+    "plt.subplot(2,2,3)\n",
+    "sns.histplot(data=df, x='avg_wordlength', hue='sentiment', kde=True)\n",
+    "plt.title('Avg Word Length')\n",
+    "\n",
+    "plt.subplot(2,2,4)\n",
+    "sns.histplot(data=df, x='stopwords_counts', hue='sentiment', kde=True)\n",
+    "plt.title('Stopwords counts')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "from sklearn.svm import LinearSVC\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import classification_report, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(df['review'], \n",
+    "                                                    df['sentiment'],\n",
+    "                                                    test_size=0.2,\n",
+    "                                                    random_state=0,\n",
+    "                                                    stratify=df['sentiment']\n",
+    "                                                   )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((40000,), (10000,))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = Pipeline([('tfidf', TfidfVectorizer()), ('clf', LinearSVC())])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\user\\anaconda\\Lib\\site-packages\\sklearn\\svm\\_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "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>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, LinearSVC())])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;, TfidfVectorizer()), (&#x27;clf&#x27;, LinearSVC())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', LinearSVC())])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    negative       0.91      0.89      0.90      5000\n",
+      "    positive       0.89      0.91      0.90      5000\n",
+      "\n",
+      "    accuracy                           0.90     10000\n",
+      "   macro avg       0.90      0.90      0.90     10000\n",
+      "weighted avg       0.90      0.90      0.90     10000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mlxtendNote: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "  Obtaining dependency information for mlxtend from https://files.pythonhosted.org/packages/1c/07/512f6a780239ad6ce06ce2aa7b4067583f5ddcfc7703a964a082c706a070/mlxtend-0.23.1-py3-none-any.whl.metadata\n",
+      "  Downloading mlxtend-0.23.1-py3-none-any.whl.metadata (7.3 kB)\n",
+      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\user\\anaconda\\lib\\site-packages (from mlxtend) (1.11.1)\n",
+      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\user\\anaconda\\lib\\site-packages (from mlxtend) (1.24.3)\n",
+      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\user\\anaconda\\lib\\site-packages (from mlxtend) (2.0.3)\n",
+      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\user\\anaconda\\lib\\site-packages (from mlxtend) (1.3.0)\n",
+      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\user\\anaconda\\lib\\site-packages (from mlxtend) (3.7.2)\n",
+      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\user\\anaconda\\lib\\site-packages (from mlxtend) (1.2.0)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.0.5)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.4.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (23.1)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (10.0.1)\n",
+      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (3.0.9)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\user\\anaconda\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\user\\anaconda\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\user\\anaconda\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2023.3)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\user\\anaconda\\lib\\site-packages (from scikit-learn>=1.0.2->mlxtend) (2.2.0)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\user\\anaconda\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.16.0)\n",
+      "Downloading mlxtend-0.23.1-py3-none-any.whl (1.4 MB)\n",
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+      "Installing collected packages: mlxtend\n",
+      "Successfully installed mlxtend-0.23.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install mlxtend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>,\n",
+       " <Axes: xlabel='predicted label', ylabel='true label'>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mlxtend.plotting import plot_confusion_matrix\n",
+    "\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "plot_confusion_matrix(cm, class_names=clf.classes_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "pickle.dump(clf, open('sentiment_analysis.pkl', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle \n",
+    "pickled_model = pickle.load(open('imdb_sentiment_analysis.pkl', 'rb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['positive'], dtype=object)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict(['Wow, this is amazing lesson'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['negative'], dtype=object)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict(['Wow, this sucks'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['positive'], dtype=object)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict(['please like this video. it helps me a lot'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['positive'], dtype=object)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict(['''nation used in the film, creates an eerie and unforgettable cinematic experience.<br /><br />The participation of Barbara Suwkowa and Eddie Constantine in the cast are two guilty pleasures that should be seen and enjoyed.\n",
+    "Max Von Sydow provides his great voice as the narrator.<br /><br />A one of a kind movie! Four stars (highest rating).\n",
+    "Before Dogma 95: when Lars used movies as art, not just a story. A beautiful painting about love and death. This is one of my favorite movies of all time. The color... The music... Just perfect.'''])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['negative'], dtype=object)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.predict([\"I did not like the movie at all\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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
+}