Codeway Task1 Movie Genre Classification .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a6fe60d-5b0e-4a49-b231-3855778b792d",
   "metadata": {},
   "source": [
    "## TASK :-1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c9f59a-b422-4d7e-8183-c87761643497",
   "metadata": {},
   "source": [
    " **Machine Learning Model That Can Predict The Genre Of A Movie**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f171574-951e-4c41-bddb-08b3aec88f7d",
   "metadata": {},
   "source": [
    "Create a Machine Learning model that can predict the genre of a movie based on its plot summary or other textual information.\n",
    "You can use technique like TF-IDF or word embeddings with classifiers such as **Naive Bayes , Logistic Regression , or Support vector Machine**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f22c2d2-715a-4d9b-b2a2-4500bdc94358",
   "metadata": {},
   "source": [
    "**Import all the packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c6cb1622",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b180f1b-908a-4fad-98ff-8315b3a86247",
   "metadata": {},
   "source": [
    "**Read the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "63bed6a4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Oscar et la dame rose (2009)</td>\n",
       "      <td>drama</td>\n",
       "      <td>Listening in to a conversation between his do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cupid (1997)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>A brother and sister with a past incestuous r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Young, Wild and Wonderful (1980)</td>\n",
       "      <td>adult</td>\n",
       "      <td>As the bus empties the students for their fie...</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <td>To help their unemployed father make ends mee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>The Unrecovered (2007)</td>\n",
       "      <td>drama</td>\n",
       "      <td>The film's title refers not only to the un-re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>54209</th>\n",
       "      <td>54210</td>\n",
       "      <td>\"Bonino\" (1953)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>This short-lived NBC live sitcom centered on ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54210</th>\n",
       "      <td>54211</td>\n",
       "      <td>Dead Girls Don't Cry (????)</td>\n",
       "      <td>horror</td>\n",
       "      <td>The NEXT Generation of EXPLOITATION. The sist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54211</th>\n",
       "      <td>54212</td>\n",
       "      <td>Ronald Goedemondt: Ze bestaan echt (2008)</td>\n",
       "      <td>documentary</td>\n",
       "      <td>Ze bestaan echt, is a stand-up comedy about g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54212</th>\n",
       "      <td>54213</td>\n",
       "      <td>Make Your Own Bed (1944)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Walter and Vivian live in the country and hav...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54213</th>\n",
       "      <td>54214</td>\n",
       "      <td>Nature's Fury: Storm of the Century (2006)</td>\n",
       "      <td>history</td>\n",
       "      <td>On Labor Day Weekend, 1935, the most intense ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54214 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0                                             1              2  \\\n",
       "0          1                 Oscar et la dame rose (2009)          drama    \n",
       "1          2                                 Cupid (1997)       thriller    \n",
       "2          3             Young, Wild and Wonderful (1980)          adult    \n",
       "3          4                        The Secret Sin (1915)          drama    \n",
       "4          5                       The Unrecovered (2007)          drama    \n",
       "...      ...                                           ...            ...   \n",
       "54209  54210                              \"Bonino\" (1953)         comedy    \n",
       "54210  54211                  Dead Girls Don't Cry (????)         horror    \n",
       "54211  54212    Ronald Goedemondt: Ze bestaan echt (2008)    documentary    \n",
       "54212  54213                     Make Your Own Bed (1944)         comedy    \n",
       "54213  54214   Nature's Fury: Storm of the Century (2006)        history    \n",
       "\n",
       "                                                       3  \n",
       "0       Listening in to a conversation between his do...  \n",
       "1       A brother and sister with a past incestuous r...  \n",
       "2       As the bus empties the students for their fie...  \n",
       "3       To help their unemployed father make ends mee...  \n",
       "4       The film's title refers not only to the un-re...  \n",
       "...                                                  ...  \n",
       "54209   This short-lived NBC live sitcom centered on ...  \n",
       "54210   The NEXT Generation of EXPLOITATION. The sist...  \n",
       "54211   Ze bestaan echt, is a stand-up comedy about g...  \n",
       "54212   Walter and Vivian live in the country and hav...  \n",
       "54213   On Labor Day Weekend, 1935, the most intense ...  \n",
       "\n",
       "[54214 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.read_csv(r\"C:\\Users\\hp\\Downloads\\train_data.txt\\train_data.txt\",sep=':::',header = None)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f7b928b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Oscar et la dame rose (2009)</td>\n",
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       "      <td>Listening in to a conversation between his do...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Cupid (1997)</td>\n",
       "      <td>thriller</td>\n",
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       "      <td>The Secret Sin (1915)</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>The Unrecovered (2007)</td>\n",
       "      <td>drama</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "   ID                               TITLE       GENRE  \\\n",
       "0   1       Oscar et la dame rose (2009)       drama    \n",
       "1   2                       Cupid (1997)    thriller    \n",
       "2   3   Young, Wild and Wonderful (1980)       adult    \n",
       "3   4              The Secret Sin (1915)       drama    \n",
       "4   5             The Unrecovered (2007)       drama    \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0   Listening in to a conversation between his do...  \n",
       "1   A brother and sister with a past incestuous r...  \n",
       "2   As the bus empties the students for their fie...  \n",
       "3   To help their unemployed father make ends mee...  \n",
       "4   The film's title refers not only to the un-re...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54214, 4)\n"
     ]
    },
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       "      <td>Edgar's Lunch (1998)</td>\n",
       "      <td>L.R. Brane loves his life - his car, his apar...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>La guerra de papá (1977)</td>\n",
       "      <td>Spain, March 1964: Quico is a very naughty ch...</td>\n",
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       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>One year in the life of Albin and his family ...</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Meu Amigo Hindu (2015)</td>\n",
       "      <td>His father has died, he hasn't spoken with hi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Er nu zhai (1955)</td>\n",
       "      <td>Before he was known internationally as a mart...</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "   ID                          TITLE  \\\n",
       "0   1          Edgar's Lunch (1998)    \n",
       "1   2      La guerra de papá (1977)    \n",
       "2   3   Off the Beaten Track (2010)    \n",
       "3   4        Meu Amigo Hindu (2015)    \n",
       "4   5             Er nu zhai (1955)    \n",
       "\n",
       "                                               GENRE  DESCRIPTION  \n",
       "0   L.R. Brane loves his life - his car, his apar...          NaN  \n",
       "1   Spain, March 1964: Quico is a very naughty ch...          NaN  \n",
       "2   One year in the life of Albin and his family ...          NaN  \n",
       "3   His father has died, he hasn't spoken with hi...          NaN  \n",
       "4   Before he was known internationally as a mart...          NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "(54200, 4)\n"
     ]
    },
    {
     "data": {
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       "    }\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>GENRE</th>\n",
       "      <th>DESCRIPTION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Edgar's Lunch (1998)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>L.R. Brane loves his life - his car, his apar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>La guerra de papá (1977)</td>\n",
       "      <td>comedy</td>\n",
       "      <td>Spain, March 1964: Quico is a very naughty ch...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Off the Beaten Track (2010)</td>\n",
       "      <td>documentary</td>\n",
       "      <td>One year in the life of Albin and his family ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Meu Amigo Hindu (2015)</td>\n",
       "      <td>drama</td>\n",
       "      <td>His father has died, he hasn't spoken with hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Er nu zhai (1955)</td>\n",
       "      <td>drama</td>\n",
       "      <td>Before he was known internationally as a mart...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                          TITLE          GENRE  \\\n",
       "0   1          Edgar's Lunch (1998)       thriller    \n",
       "1   2      La guerra de papá (1977)         comedy    \n",
       "2   3   Off the Beaten Track (2010)    documentary    \n",
       "3   4        Meu Amigo Hindu (2015)          drama    \n",
       "4   5             Er nu zhai (1955)          drama    \n",
       "\n",
       "                                         DESCRIPTION  \n",
       "0   L.R. Brane loves his life - his car, his apar...  \n",
       "1   Spain, March 1964: Quico is a very naughty ch...  \n",
       "2   One year in the life of Albin and his family ...  \n",
       "3   His father has died, he hasn't spoken with hi...  \n",
       "4   Before he was known internationally as a mart...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "(54200, 4)\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv(r\"C:\\Users\\hp\\Downloads\\train_data.txt\\train_data.txt\",sep=':::',header = None, names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "display(train_data.head())\n",
    "print(train_data.shape)\n",
    "\n",
    "\n",
    "test_data = pd.read_csv(r\"C:\\Users\\hp\\Downloads\\test_data.txt\\test_data.txt\",sep=':::',header=None, names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "print(display(test_data.head()))\n",
    "print(test_data.shape)\n",
    "\n",
    "test_solution_data = pd.read_csv(r\"C:\\Users\\hp\\Downloads\\test_data_solution.txt\\test_data_solution.txt\",sep=':::', names=['ID', 'TITLE', 'GENRE', 'DESCRIPTION'])\n",
    "print(display(test_solution_data.head()))\n",
    "print(test_solution_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be60a9f2",
   "metadata": {},
   "source": [
    "## Data visualization method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f635f72c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "sns.countplot(y=train_data['GENRE'], order = train_data['GENRE'].value_counts().index)\n",
    "plt.title('Number of Movies per Genre')\n",
    "plt.xlabel('Number of Movies')\n",
    "plt.ylabel('Genre')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba8ed4d",
   "metadata": {},
   "source": [
    "## Bar Representation of Genre vs Description length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3bb3901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['DESCRIPTION_length'] = train_data['DESCRIPTION'].apply(len)\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.barplot(x='GENRE', y='DESCRIPTION_length', data=train_data)\n",
    "plt.title('Description Length by Genre')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Genre')\n",
    "plt.ylabel('Description Length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bee058",
   "metadata": {},
   "source": [
    "## Top genre which mostly people watched"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a37a8ee",
   "metadata": {},
   "source": [
    "#### Train And Test the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f4fe801",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle any potential missing values\n",
    "train_data['DESCRIPTION'].fillna(\"\", inplace=True)\n",
    "test_data['DESCRIPTION'].fillna(\"\", inplace=True)\n",
    "\n",
    "t_v = TfidfVectorizer(stop_words='english', max_features=100000)\n",
    "X_train = t_v.fit_transform(train_data['DESCRIPTION'])\n",
    "X_test = t_v.transform(test_data['DESCRIPTION'])\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(train_data['GENRE'])\n",
    "y_test = label_encoder.transform(test_solution_data['GENRE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c98848b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy: 0.5836945494789265\n",
      "Validation Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.32      0.37       263\n",
      "           1       0.74      0.44      0.55       112\n",
      "           2       0.45      0.21      0.28       139\n",
      "           3       0.47      0.15      0.23       104\n",
      "           4       0.00      0.00      0.00        61\n",
      "           5       0.53      0.59      0.56      1443\n",
      "           6       0.39      0.07      0.11       107\n",
      "           7       0.69      0.81      0.75      2659\n",
      "           8       0.56      0.72      0.63      2697\n",
      "           9       0.36      0.17      0.23       150\n",
      "          10       0.13      0.03      0.04        74\n",
      "          11       0.82      0.68      0.74        40\n",
      "          12       0.00      0.00      0.00        45\n",
      "          13       0.65      0.66      0.66       431\n",
      "          14       0.61      0.53      0.57       144\n",
      "          15       0.25      0.04      0.07        50\n",
      "          16       0.43      0.05      0.10        56\n",
      "          17       0.20      0.06      0.09        34\n",
      "          18       0.49      0.25      0.33       192\n",
      "          19       0.36      0.06      0.10       151\n",
      "          20       0.50      0.28      0.36       143\n",
      "          21       0.44      0.36      0.40      1045\n",
      "          22       0.60      0.41      0.49        93\n",
      "          23       0.62      0.25      0.35        81\n",
      "          24       0.30      0.16      0.21       309\n",
      "          25       0.50      0.05      0.09        20\n",
      "          26       0.85      0.83      0.84       200\n",
      "\n",
      "    accuracy                           0.58     10843\n",
      "   macro avg       0.46      0.30      0.34     10843\n",
      "weighted avg       0.56      0.58      0.56     10843\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train_sub, X_val, y_train_sub, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "clf = LinearSVC()\n",
    "clf.fit(X_train_sub, y_train_sub)\n",
    "\n",
    "y_val_pred = clf.predict(X_val)\n",
    "print(\"Validation Accuracy:\", accuracy_score(y_val, y_val_pred))\n",
    "print(\"Validation Classification Report:\\n\", classification_report(y_val, y_val_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "432dbdd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.09357933579335793\n",
      "Test Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      1314\n",
      "           1       0.00      0.00      0.00       590\n",
      "           2       0.00      0.00      0.00       775\n",
      "           3       0.00      0.00      0.00       498\n",
      "           4       0.00      0.00      0.00       264\n",
      "           5       0.00      0.00      0.00      7446\n",
      "           6       0.00      0.00      0.00       505\n",
      "           7       0.00      0.00      0.00     13096\n",
      "           8       0.00      0.00      0.00     13612\n",
      "           9       0.00      0.00      0.00       783\n",
      "          10       0.00      0.00      0.00       322\n",
      "          11       0.00      0.00      0.00       193\n",
      "          12       0.00      0.00      0.00       243\n",
      "          13       0.00      0.00      0.00      2204\n",
      "          14       0.00      0.00      0.00       731\n",
      "          15       0.00      0.00      0.00       276\n",
      "          16       0.00      0.00      0.00       318\n",
      "          17       0.00      0.00      0.00       181\n",
      "          18       0.00      0.00      0.00       883\n",
      "          19       0.00      0.00      0.00       672\n",
      "          20       0.00      0.00      0.00       646\n",
      "          21       0.09      1.00      0.17      5072\n",
      "          22       0.00      0.00      0.00       431\n",
      "          23       0.00      0.00      0.00       391\n",
      "          24       0.00      0.00      0.00      1590\n",
      "          25       0.00      0.00      0.00       132\n",
      "          26       0.00      0.00      0.00      1032\n",
      "\n",
      "    accuracy                           0.09     54200\n",
      "   macro avg       0.00      0.04      0.01     54200\n",
      "weighted avg       0.01      0.09      0.02     54200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print(\"Test Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"Test Classification Report:\\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73095351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "Mnb_classifier = MultinomialNB()\n",
    "Mnb_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8cfb6bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mnb_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab919978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=500)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=500)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_classifier = LogisticRegression(max_iter=500)\n",
    "lr_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d24f226d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 8, 8, ..., 8, 8, 8])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b8fd9d3",
   "metadata": {},
   "source": [
    "#### Designing a function show that we can predict the genre of the movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fdfcd27a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " action \n",
      " drama \n"
     ]
    }
   ],
   "source": [
    "def predict_movie(description):\n",
    "    t_v1 = t_v.transform([description])\n",
    "    pred_label = clf.predict(t_v1)\n",
    "    return label_encoder.inverse_transform(pred_label)[0]\n",
    "\n",
    "sample_descr_for_movie = \"A movie where police cashes the criminal and shoot him\"\n",
    "print(predict_movie(sample_descr_for_movie))\n",
    "\n",
    "sample_descr_for_movie1 = \"A movie where person cashes a girl too get marry with him but girl refuses him.\"\n",
    "print(predict_movie(sample_descr_for_movie1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faa6ca56",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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