genre-classification.ipynb

{
 "cells": [
  {
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: pytz, pygtrie, funcy, dictdiffer, appdirs, antlr4-python3-runtime, zipp, wrapt, waitress, voluptuous, vine, urllib3, tzdata, typing-extensions, treelib, tqdm, tomlkit, threadpoolctl, tenacity, tabulate, sqlparse, sniffio, smmap, shtab, shortuuid, shellingham, setuptools, semver, ruamel.yaml.clib, regex, pyyaml, python-dotenv, pyparsing, pycparser, pyasn1, pyarrow, protobuf, propcache, pillow, pathvalidate, pathspec, orjson, numpy, networkx, mypy-extensions, multidict, mdurl, marshmallow, MarkupSafe, markdown, lxml, kiwisolver, joblib, jmespath, itsdangerous, idna, h11, greenlet, graphql-core, fsspec, frozenlist, fonttools, flatten-dict, filelock, entrypoints, dvc-render, dpath, distro, diskcache, dacite, cycler, configobj, cloudpickle, click, charset-normalizer, certifi, cachetools, blinker, billiard, backoff, attrs, atpublic, annotated-types, aioitertools, aiohappyeyeballs, zc.lockfile, yarl, Werkzeug, typing-inspect, sqltrie, sqlalchemy, scipy, ruamel.yaml, rsa, requests, pydot, pydantic-core, pyasn1-modules, pandas, omegaconf, nltk, markdown-it-py, Mako, Jinja2, importlib_metadata, httpcore, graphql-relay, grandalf, gitdb, flufl.lock, dvc-objects, dulwich, deprecated, contourpy, click-repl, click-plugins, click-didyoumean, cffi, botocore, anyio, amqp, aiosignal, scikit-learn, s3transfer, rich, requests-toolbelt, pygit2, pydantic, opentelemetry-api, matplotlib, kombu, iterative-telemetry, hydra-core, httpx, graphene, gql, google-auth, gitpython, flask, dvc-studio-client, dvc-data, docker, dataclasses-json, cryptography, alembic, aiohttp, wordcloud, typer, seaborn, opentelemetry-semantic-conventions, databricks-sdk, dagshub-annotation-converter, celery, boto3, asyncssh, aiohttp-retry, aiobotocore, scmrepo, s3fs, opentelemetry-sdk, dvc-task, dvc-http, dagshub, mlflow-skinny, gto, mlflow, dvc, dvc_s3\n",
      "Successfully installed Jinja2-3.1.5 Mako-1.3.8 MarkupSafe-3.0.2 Werkzeug-3.1.3 aiobotocore-2.16.0 aiohappyeyeballs-2.4.4 aiohttp-3.11.11 aiohttp-retry-2.9.1 aioitertools-0.12.0 aiosignal-1.3.2 alembic-1.14.0 amqp-5.3.1 annotated-types-0.7.0 antlr4-python3-runtime-4.9.3 anyio-4.7.0 appdirs-1.4.4 asyncssh-2.19.0 atpublic-5.0 attrs-24.3.0 backoff-2.2.1 billiard-4.2.1 blinker-1.9.0 boto3-1.35.81 botocore-1.35.81 cachetools-5.5.0 celery-5.4.0 certifi-2024.12.14 cffi-1.17.1 charset-normalizer-3.4.0 click-8.1.8 click-didyoumean-0.3.1 click-plugins-1.1.1 click-repl-0.3.0 cloudpickle-3.1.0 configobj-5.0.9 contourpy-1.3.1 cryptography-44.0.0 cycler-0.12.1 dacite-1.6.0 dagshub-0.4.0 dagshub-annotation-converter-0.1.2 databricks-sdk-0.40.0 dataclasses-json-0.6.7 deprecated-1.2.15 dictdiffer-0.9.0 diskcache-5.6.3 distro-1.9.0 docker-7.1.0 dpath-2.2.0 dulwich-0.22.7 dvc-3.58.0 dvc-data-3.16.7 dvc-http-2.32.0 dvc-objects-5.1.0 dvc-render-1.0.2 dvc-studio-client-0.21.0 dvc-task-0.40.2 dvc_s3-3.2.0 entrypoints-0.4 filelock-3.16.1 flask-3.1.0 flatten-dict-0.4.2 flufl.lock-8.1.0 fonttools-4.55.3 frozenlist-1.5.0 fsspec-2024.12.0 funcy-2.0 gitdb-4.0.11 gitpython-3.1.43 google-auth-2.37.0 gql-3.5.0 grandalf-0.8 graphene-3.4.3 graphql-core-3.2.5 graphql-relay-3.2.0 greenlet-3.1.1 gto-1.7.2 h11-0.14.0 httpcore-1.0.7 httpx-0.28.1 hydra-core-1.3.2 idna-3.10 importlib_metadata-8.5.0 iterative-telemetry-0.0.9 itsdangerous-2.2.0 jmespath-1.0.1 joblib-1.4.2 kiwisolver-1.4.7 kombu-5.4.2 lxml-5.3.0 markdown-3.7 markdown-it-py-3.0.0 marshmallow-3.23.2 matplotlib-3.10.0 mdurl-0.1.2 mlflow-2.19.0 mlflow-skinny-2.19.0 multidict-6.1.0 mypy-extensions-1.0.0 networkx-3.4.2 nltk-3.9.1 numpy-2.2.1 omegaconf-2.3.0 opentelemetry-api-1.29.0 opentelemetry-sdk-1.29.0 opentelemetry-semantic-conventions-0.50b0 orjson-3.10.12 pandas-2.2.3 pathspec-0.12.1 pathvalidate-3.2.1 pillow-11.0.0 propcache-0.2.1 protobuf-5.29.2 pyarrow-18.1.0 pyasn1-0.6.1 pyasn1-modules-0.4.1 pycparser-2.22 pydantic-2.10.4 pydantic-core-2.27.2 pydot-3.0.3 pygit2-1.16.0 pygtrie-2.5.0 pyparsing-3.2.0 python-dotenv-1.0.1 pytz-2024.2 pyyaml-6.0.2 regex-2024.11.6 requests-2.32.3 requests-toolbelt-1.0.0 rich-13.9.4 rsa-4.9 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.12 s3fs-2024.12.0 s3transfer-0.10.4 scikit-learn-1.6.0 scipy-1.14.1 scmrepo-3.3.9 seaborn-0.13.2 semver-3.0.2 setuptools-75.6.0 shellingham-1.5.4 shortuuid-1.0.13 shtab-1.7.1 smmap-5.0.1 sniffio-1.3.1 sqlalchemy-2.0.36 sqlparse-0.5.3 sqltrie-0.11.1 tabulate-0.9.0 tenacity-9.0.0 threadpoolctl-3.5.0 tomlkit-0.13.2 tqdm-4.67.1 treelib-1.7.0 typer-0.15.1 typing-extensions-4.12.2 typing-inspect-0.9.0 tzdata-2024.2 urllib3-2.3.0 vine-5.1.0 voluptuous-0.15.2 waitress-3.0.2 wordcloud-1.9.4 wrapt-1.17.0 yarl-1.18.3 zc.lockfile-3.0.post1 zipp-3.21.0\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\bhavy\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\bhavy\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\bhavy\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB \n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, r2_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from wordcloud import WordCloud\n",
    "from collections import Counter\n",
    "from nltk.stem import PorterStemmer\n",
    "import string as st\n",
    "import re\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhavy\\AppData\\Local\\Temp\\ipykernel_18408\\2481135764.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  test_data = pd.read_csv(\"data/test_data.txt\", sep = ':::', header = None)\n",
      "C:\\Users\\bhavy\\AppData\\Local\\Temp\\ipykernel_18408\\2481135764.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  train_data = pd.read_csv(\"data/train_data.txt\", sep = ':::', header = None)\n",
      "C:\\Users\\bhavy\\AppData\\Local\\Temp\\ipykernel_18408\\2481135764.py:3: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
      "  test_data_solution = pd.read_csv(\"data/test_data_solution.txt\", sep = ':::', header = None)\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"data/test_data.txt\", sep = ':::', header = None)\n",
    "train_data = pd.read_csv(\"data/train_data.txt\", sep = ':::', header = None)\n",
    "test_data_solution = pd.read_csv(\"data/test_data_solution.txt\", sep = ':::', header = None)\n",
    "\n",
    "train_data.columns = [\"ID\", \"Title\", \"Genre\", \"Description\"]\n",
    "test_data.columns = [\"ID\", \"Title\", \"Description\"]\n",
    "test_data_solution.columns = [\"ID\", \"Title\", \"Genre\", \"Description\"]\n",
    "\n",
    "# Merge genre from test data solution with test data based on movie name\n",
    "test_data = pd.merge(test_data, test_data_solution[[\"Title\", \"Genre\"]], on = \"Title\", how = \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>Genre</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>L.R. Brane loves his life - his car, his apar...</td>\n",
       "      <td>thriller</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",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Off the Beaten Track (2010)</td>\n",
       "      <td>One year in the life of Albin and his family ...</td>\n",
       "      <td>documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>drama</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>drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "                                         Description          Genre  \n",
       "0   L.R. Brane loves his life - his car, his apar...      thriller   \n",
       "1   Spain, March 1964: Quico is a very naughty ch...        comedy   \n",
       "2   One year in the life of Albin and his family ...   documentary   \n",
       "3   His father has died, he hasn't spoken with hi...         drama   \n",
       "4   Before he was known internationally as a mart...         drama   "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "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>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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>The Secret Sin (1915)</td>\n",
       "      <td>drama</td>\n",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Shape:  (54214, 4)\n",
      "Test Data Shape:  (54200, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Data Shape: \",train_data.shape)\n",
    "print(\"Test Data Shape: \",test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Info: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54200 entries, 0 to 54199\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   ID           54200 non-null  int64 \n",
      " 1   Title        54200 non-null  object\n",
      " 2   Description  54200 non-null  object\n",
      " 3   Genre        54200 non-null  object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 1.7+ MB\n",
      "None\n",
      "Train data info: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54214 entries, 0 to 54213\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   ID           54214 non-null  int64 \n",
      " 1   Title        54214 non-null  object\n",
      " 2   Genre        54214 non-null  object\n",
      " 3   Description  54214 non-null  object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 1.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Data Info: \")\n",
    "print(test_data.info())\n",
    "print(\"Train data info: \")\n",
    "print(train_data.info())\n",
    "# We don't have any statistical data in this situation, so not using describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Null Values: \n",
      "ID             0\n",
      "Title          0\n",
      "Description    0\n",
      "Genre          0\n",
      "dtype: int64\n",
      "Train Data Null Values: \n",
      "ID             0\n",
      "Title          0\n",
      "Genre          0\n",
      "Description    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Data Null Values: \")\n",
    "print(test_data.isnull().sum())\n",
    "print(\"Train Data Null Values: \")\n",
    "print(train_data.isnull().sum())\n",
    "# No null values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of Genres in Train Data: \n",
      "Genre\n",
      "drama           13613\n",
      "documentary     13096\n",
      "comedy           7447\n",
      "short            5073\n",
      "horror           2204\n",
      "thriller         1591\n",
      "action           1315\n",
      "western          1032\n",
      "reality-tv        884\n",
      "family            784\n",
      "adventure         775\n",
      "music             731\n",
      "romance           672\n",
      "sci-fi            647\n",
      "adult             590\n",
      "crime             505\n",
      "animation         498\n",
      "sport             432\n",
      "talk-show         391\n",
      "fantasy           323\n",
      "mystery           319\n",
      "musical           277\n",
      "biography         265\n",
      "history           243\n",
      "game-show         194\n",
      "news              181\n",
      "war               132\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Type of Genres in Dataset\n",
    "print(\"Type of Genres in Train Data: \")\n",
    "genre_counts = train_data[\"Genre\"].value_counts()\n",
    "print(genre_counts)\n",
    "# Most common genre includes drama and documentary and least common genre includes war, news and game-shows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution\n",
    "plt.figure(figsize=(10,5))\n",
    "genre_counts.plot(kind='bar')\n",
    "plt.title(\"Genre Distribution\")\n",
    "plt.xlabel(\"Genres\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see the maximum hype is for drama, documentary, comedy and short\n",
    "- Moderate hype for thriller, horror and action\n",
    "- And others have an average hype with min for war"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Description</th>\n",
       "      <th>Description_Length</th>\n",
       "    </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",
       "      <td>92</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",
       "      <td>32</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",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>The Secret Sin (1915)</td>\n",
       "      <td>drama</td>\n",
       "      <td>To help their unemployed father make ends mee...</td>\n",
       "      <td>192</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",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  Description_Length  \n",
       "0   Listening in to a conversation between his do...                  92  \n",
       "1   A brother and sister with a past incestuous r...                  32  \n",
       "2   As the bus empties the students for their fie...                 114  \n",
       "3   To help their unemployed father make ends mee...                 192  \n",
       "4   The film's title refers not only to the un-re...                 106  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count length of each description and add a column of that in train data\n",
    "train_data[\"Description_Length\"] = train_data[\"Description\"].apply(lambda x: len(x.split()))\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the word count distribution\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.histplot(train_data[\"Description_Length\"], bins = 30,kde=True)\n",
    "plt.title(\"Word Count Distribution\")\n",
    "plt.xlabel(\"Number of Words\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Genre vs Description Length\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.lineplot(x = train_data[\"Genre\"], y = train_data[\"Description_Length\"])\n",
    "plt.title(\"Text Length by Genre\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Genres:  [' drama ' ' thriller ' ' adult ' ' documentary ' ' comedy ' ' crime '\n",
      " ' reality-tv ' ' horror ' ' sport ' ' animation ' ' action ' ' fantasy '\n",
      " ' short ' ' sci-fi ' ' music ' ' adventure ' ' talk-show ' ' western '\n",
      " ' family ' ' mystery ' ' history ' ' news ' ' biography ' ' romance '\n",
      " ' game-show ' ' musical ' ' war ']\n"
     ]
    }
   ],
   "source": [
    "# List all unique genres in the dataset\n",
    "genres = train_data[\"Genre\"].unique()\n",
    "print(\"Unique Genres: \",genres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('the', 333793), (',', 310018), ('.', 248697), ('and', 190634), ('a', 175928), ('of', 167220), ('to', 158834), ('in', 108715), ('is', 83270), ('his', 67668), (\"'s\", 53266), ('with', 52633), ('her', 46967), ('he', 45242), ('for', 41771), ('that', 41142), ('on', 36877), ('as', 35275), ('their', 32370), ('by', 30801)]\n"
     ]
    }
   ],
   "source": [
    "# Tokenize all plot summaries and count word frequencies\n",
    "all_words = [word for text in train_data['Description'] for word in word_tokenize(text.lower())]\n",
    "word_freq = Counter(all_words)\n",
    "\n",
    "# Display most common words\n",
    "print(word_freq.most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(columns = [\"Description_Length\"], inplace = True)\n",
    "def preprocess_text(text):\n",
    "    text = text.lower() # Lowercase\n",
    "    text = re.sub(r'[^a-z\\s]', '', text) # Remove special characters and digits\n",
    "    tokens = word_tokenize(text) # Tokenize\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = [word for word in tokens if word not in stop_words]\n",
    "    lemmatiser = WordNetLemmatizer()\n",
    "    cleaned_text = \" \".join(lemmatiser.lemmatize(token) for token in tokens)\n",
    "    return cleaned_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Description'] = train_data['Description'].apply(preprocess_text)\n",
    "test_data['Description'] = test_data['Description'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>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>Oscar et la dame rose (2009)</td>\n",
       "      <td>drama</td>\n",
       "      <td>listening conversation doctor parent yearold o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cupid (1997)</td>\n",
       "      <td>thriller</td>\n",
       "      <td>brother sister past incestuous relationship cu...</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>bus empty student field trip museum natural hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>The Secret Sin (1915)</td>\n",
       "      <td>drama</td>\n",
       "      <td>help unemployed father make end meet edith twi...</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>film title refers unrecovered body ground zero...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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 conversation doctor parent yearold o...  \n",
       "1  brother sister past incestuous relationship cu...  \n",
       "2  bus empty student field trip museum natural hi...  \n",
       "3  help unemployed father make end meet edith twi...  \n",
       "4  film title refers unrecovered body ground zero...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>ID</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>Genre</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>lr brane love life car apartment job especiall...</td>\n",
       "      <td>thriller</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 quico naughty child three belongin...</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Off the Beaten Track (2010)</td>\n",
       "      <td>one year life albin family shepherd north tran...</td>\n",
       "      <td>documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Meu Amigo Hindu (2015)</td>\n",
       "      <td>father died hasnt spoken brother year serious ...</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Er nu zhai (1955)</td>\n",
       "      <td>known internationally martial art superstar br...</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "                                         Description          Genre  \n",
       "0  lr brane love life car apartment job especiall...      thriller   \n",
       "1  spain march quico naughty child three belongin...        comedy   \n",
       "2  one year life albin family shepherd north tran...   documentary   \n",
       "3  father died hasnt spoken brother year serious ...         drama   \n",
       "4  known internationally martial art superstar br...         drama   "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('life', 23908), ('one', 17442), ('film', 14643), ('story', 12677), ('year', 11967), ('world', 11632), ('new', 11528), ('find', 11311), ('family', 10805), ('get', 10121), ('young', 10100), ('love', 10043), ('time', 9843), ('take', 9530), ('two', 9488), ('friend', 9171), ('woman', 9101), ('man', 9094), ('people', 8185), ('day', 8117)]\n"
     ]
    }
   ],
   "source": [
    "# Tokenize all plot summaries and count word frequencies\n",
    "all_words = [word for text in train_data['Description'] for word in word_tokenize(text.lower())]\n",
    "word_freq = Counter(all_words)\n",
    "\n",
    "# Display most common words\n",
    "print(word_freq.most_common(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# count length of each description and add a column of that in train data\n",
    "train_description_scaler=pd.DataFrame()\n",
    "train_description_scaler[\"Description_Length\"] = train_data[\"Description\"].apply(lambda x: len(x.split()))\n",
    "scaler = MinMaxScaler()\n",
    "train_data[\"Description_Length\"] = scaler.fit_transform(train_description_scaler[\"Description_Length\"].values.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize TfidfVectorizer with reduced max_features\n",
    "tfidf_vectorizer = TfidfVectorizer(max_features=70000)  # Reduce dimensionality\n",
    "X_train = tfidf_vectorizer.fit_transform(train_data['Description'])  # Sparse matrix\n",
    "X_test = tfidf_vectorizer.transform(test_data['Description']) # Transform the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((54214, 70000), (54200, 70000))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding the target labels\n",
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(train_data['Genre'])\n",
    "y_test = label_encoder.transform(test_data['Genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 8, 24,  1, ...,  7,  5, 12], shape=(54214,)),\n",
       " array([24,  5,  7, ...,  1,  8,  8], shape=(54200,)))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_split, X_val_split, y_train_split, y_val_split = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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: #000;\n",
       "  --sklearn-color-text-muted: #666;\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: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
       "  text-align: center;\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\"><div><div>MultinomialNB</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/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></div></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model = MultinomialNB()\n",
    "nb_model.fit(X_train_split, y_train_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Model Evaluation:\n",
      "Accuracy: 0.4477\n",
      "\n",
      "Classification Report:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      action        0.00      0.00      0.00       263\n",
      "       adult        0.00      0.00      0.00       112\n",
      "   adventure        0.00      0.00      0.00       139\n",
      "   animation        0.00      0.00      0.00       104\n",
      "   biography        0.00      0.00      0.00        61\n",
      "      comedy        0.60      0.08      0.14      1443\n",
      "       crime        0.00      0.00      0.00       107\n",
      " documentary        0.53      0.90      0.67      2659\n",
      "       drama        0.38      0.87      0.53      2697\n",
      "      family        0.00      0.00      0.00       150\n",
      "     fantasy        0.00      0.00      0.00        74\n",
      "   game-show        0.00      0.00      0.00        40\n",
      "     history        0.00      0.00      0.00        45\n",
      "      horror        1.00      0.00      0.01       431\n",
      "       music        0.00      0.00      0.00       144\n",
      "     musical        0.00      0.00      0.00        50\n",
      "     mystery        0.00      0.00      0.00        56\n",
      "        news        0.00      0.00      0.00        34\n",
      "  reality-tv        0.00      0.00      0.00       192\n",
      "     romance        0.00      0.00      0.00       151\n",
      "      sci-fi        0.00      0.00      0.00       143\n",
      "       short        0.75      0.00      0.01      1045\n",
      "       sport        0.00      0.00      0.00        93\n",
      "   talk-show        0.00      0.00      0.00        81\n",
      "    thriller        0.00      0.00      0.00       309\n",
      "         war        0.00      0.00      0.00        20\n",
      "     western        0.00      0.00      0.00       200\n",
      "\n",
      "     accuracy                           0.45     10843\n",
      "    macro avg       0.12      0.07      0.05     10843\n",
      " weighted avg       0.42      0.45      0.31     10843\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# Predict on the validation set\n",
    "y_val_pred = nb_model.predict(X_val_split)\n",
    "\n",
    "# Evaluate the model's performance\n",
    "print(\"Naive Bayes Model Evaluation:\")\n",
    "print(f\"Accuracy: {accuracy_score(y_val_split, y_val_pred):.4f}\\n\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_val_split, y_val_pred, target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
       "  text-align: center;\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()</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\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/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></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(X_train_split, y_train_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model Evaluation:\n",
      "Accuracy: 0.5798\n",
      "\n",
      "Classification Report:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      action        0.55      0.21      0.31       263\n",
      "       adult        0.78      0.19      0.30       112\n",
      "   adventure        0.45      0.13      0.20       139\n",
      "   animation        0.50      0.01      0.02       104\n",
      "   biography        0.00      0.00      0.00        61\n",
      "      comedy        0.52      0.60      0.56      1443\n",
      "       crime        0.33      0.01      0.02       107\n",
      " documentary        0.66      0.86      0.75      2659\n",
      "       drama        0.53      0.80      0.64      2697\n",
      "      family        0.53      0.07      0.12       150\n",
      "     fantasy        0.00      0.00      0.00        74\n",
      "   game-show        0.93      0.35      0.51        40\n",
      "     history        0.00      0.00      0.00        45\n",
      "      horror        0.68      0.55      0.61       431\n",
      "       music        0.66      0.38      0.48       144\n",
      "     musical        0.00      0.00      0.00        50\n",
      "     mystery        0.00      0.00      0.00        56\n",
      "        news        0.00      0.00      0.00        34\n",
      "  reality-tv        0.50      0.11      0.19       192\n",
      "     romance        0.00      0.00      0.00       151\n",
      "      sci-fi        0.59      0.20      0.30       143\n",
      "       short        0.49      0.29      0.37      1045\n",
      "       sport        0.74      0.18      0.29        93\n",
      "   talk-show        0.68      0.16      0.26        81\n",
      "    thriller        0.47      0.12      0.19       309\n",
      "         war        0.00      0.00      0.00        20\n",
      "     western        0.95      0.69      0.80       200\n",
      "\n",
      "     accuracy                           0.58     10843\n",
      "    macro avg       0.43      0.22      0.26     10843\n",
      " weighted avg       0.55      0.58      0.53     10843\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# Predict on the validation set\n",
    "y_val_pred = lr_model.predict(X_val_split)\n",
    "\n",
    "# Evaluate the model's performance\n",
    "print(\"Logistic Regression Model Evaluation:\")\n",
    "print(f\"Accuracy: {accuracy_score(y_val_split, y_val_pred):.4f}\\n\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_val_split, y_val_pred, target_names=label_encoder.classes_))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear SVC Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 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: 0.5em;\n",
       "  text-align: center;\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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearSVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model = LinearSVC()\n",
    "svc_model.fit(X_train_split, y_train_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVC Model Evaluation:\n",
      "Accuracy: 0.5786\n",
      "\n",
      "Classification Report:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      action        0.47      0.35      0.40       263\n",
      "       adult        0.76      0.46      0.58       112\n",
      "   adventure        0.44      0.24      0.31       139\n",
      "   animation        0.26      0.10      0.14       104\n",
      "   biography        0.00      0.00      0.00        61\n",
      "      comedy        0.51      0.58      0.55      1443\n",
      "       crime        0.30      0.07      0.12       107\n",
      " documentary        0.69      0.81      0.74      2659\n",
      "       drama        0.56      0.72      0.63      2697\n",
      "      family        0.34      0.15      0.21       150\n",
      "     fantasy        0.36      0.07      0.11        74\n",
      "   game-show        0.89      0.62      0.74        40\n",
      "     history        0.50      0.02      0.04        45\n",
      "      horror        0.64      0.64      0.64       431\n",
      "       music        0.59      0.56      0.58       144\n",
      "     musical        0.33      0.06      0.10        50\n",
      "     mystery        0.67      0.04      0.07        56\n",
      "        news        0.17      0.03      0.05        34\n",
      "  reality-tv        0.48      0.26      0.34       192\n",
      "     romance        0.26      0.04      0.07       151\n",
      "      sci-fi        0.49      0.27      0.35       143\n",
      "       short        0.43      0.35      0.38      1045\n",
      "       sport        0.61      0.42      0.50        93\n",
      "   talk-show        0.52      0.28      0.37        81\n",
      "    thriller        0.32      0.17      0.23       309\n",
      "         war        0.25      0.05      0.08        20\n",
      "     western        0.85      0.82      0.83       200\n",
      "\n",
      "     accuracy                           0.58     10843\n",
      "    macro avg       0.47      0.30      0.34     10843\n",
      " weighted avg       0.55      0.58      0.55     10843\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict on the validation set\n",
    "y_val_pred = svc_model.predict(X_val_split)\n",
    "\n",
    "# Evaluate the model's performance\n",
    "print(\"Linear SVC Model Evaluation:\")\n",
    "print(f\"Accuracy: {accuracy_score(y_val_split, y_val_pred):.4f}\\n\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_val_split, y_val_pred, target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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: 0.5em;\n",
       "  text-align: center;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=-1, random_state=42)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random forest classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model = RandomForestClassifier(\n",
    "    n_estimators=100,  # Number of trees\n",
    "    max_depth=None,    # No depth limit\n",
    "    random_state=42,   # For reproducibility\n",
    "    n_jobs=-1          # Utilize all CPU cores for faster training\n",
    ")\n",
    "rf_model.fit(X_train_split, y_train_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Model Evaluation:\n",
      "Accuracy: 0.4878\n",
      "\n",
      "Classification Report:\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "      action        0.00      0.00      0.00       263\n",
      "       adult        1.00      0.09      0.16       112\n",
      "   adventure        0.67      0.06      0.11       139\n",
      "   animation        0.00      0.00      0.00       104\n",
      "   biography        0.00      0.00      0.00        61\n",
      "      comedy        0.53      0.27      0.35      1443\n",
      "       crime        1.00      0.01      0.02       107\n",
      " documentary        0.55      0.89      0.68      2659\n",
      "       drama        0.42      0.84      0.56      2697\n",
      "      family        1.00      0.03      0.05       150\n",
      "     fantasy        0.00      0.00      0.00        74\n",
      "   game-show        0.92      0.30      0.45        40\n",
      "     history        0.00      0.00      0.00        45\n",
      "      horror        0.81      0.11      0.19       431\n",
      "       music        0.77      0.12      0.20       144\n",
      "     musical        0.00      0.00      0.00        50\n",
      "     mystery        0.00      0.00      0.00        56\n",
      "        news        0.00      0.00      0.00        34\n",
      "  reality-tv        0.00      0.00      0.00       192\n",
      "     romance        0.00      0.00      0.00       151\n",
      "      sci-fi        0.75      0.02      0.04       143\n",
      "       short        0.66      0.07      0.13      1045\n",
      "       sport        0.71      0.05      0.10        93\n",
      "   talk-show        1.00      0.02      0.05        81\n",
      "    thriller        1.00      0.00      0.01       309\n",
      "         war        0.00      0.00      0.00        20\n",
      "     western        0.95      0.46      0.61       200\n",
      "\n",
      "     accuracy                           0.49     10843\n",
      "    macro avg       0.47      0.12      0.14     10843\n",
      " weighted avg       0.53      0.49      0.39     10843\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# Predict on the validation set\n",
    "y_val_pred = rf_model.predict(X_val_split)\n",
    "# Evaluate the model's performance\n",
    "print(\"Random Forest Model Evaluation:\")\n",
    "print(f\"Accuracy: {accuracy_score(y_val_split, y_val_pred):.4f}\\n\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_val_split, y_val_pred, target_names=label_encoder.classes_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finetuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
      "Best parameters for Linear SVC: {'C': 0.5, 'max_iter': 500}\n",
      "Best accuracy for Linear SVC: 0.5792349726775957\n",
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n",
      "Best parameters for Logistic Regression: {'C': 5, 'max_iter': 100, 'solver': 'lbfgs'}\n",
      "Best accuracy for Logistic Regression: 0.5826473911138779\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Fine-tuning Linear SVC\n",
    "svc_params = {\n",
    "    'C': [0.05, 0.1, 0.5, 1],  # Narrowed range\n",
    "    'max_iter': [500, 1000],  # Increased iterations for robustness\n",
    "}\n",
    "svc_model = LinearSVC()\n",
    "svc_grid = GridSearchCV(svc_model, svc_params, cv=3, scoring='accuracy', verbose=2, n_jobs=-1)\n",
    "svc_grid.fit(X_train_split, y_train_split)\n",
    "\n",
    "print(\"Best parameters for Linear SVC:\", svc_grid.best_params_)\n",
    "print(\"Best accuracy for Linear SVC:\", svc_grid.best_score_)\n",
    "\n",
    "# Fine-tuning Logistic Regression\n",
    "lr_params = {\n",
    "    'C': [1, 5, 10, 50],  # Adjusted range\n",
    "    'max_iter': [100, 200, 500],  # Increased iterations\n",
    "    'solver': ['lbfgs', 'saga'],  # Using solvers for larger datasets\n",
    "}\n",
    "lr_model = LogisticRegression()\n",
    "lr_grid = GridSearchCV(lr_model, lr_params, cv=3, scoring='accuracy', verbose=2, n_jobs=-1)\n",
    "lr_grid.fit(X_train_split, y_train_split)\n",
    "\n",
    "print(\"Best parameters for Logistic Regression:\", lr_grid.best_params_)\n",
    "print(\"Best accuracy for Logistic Regression:\", lr_grid.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Model: Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensemble Model Accuracy: 0.5898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\bhavy\\Desktop\\Projects\\codsoft-ml-internship-projects\\1-movie-genre-classification\\venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Best models from fine-tuning\n",
    "svc_best = LinearSVC(C=0.5, max_iter=500)\n",
    "lr_best = LogisticRegression(C=5, max_iter=100, solver='lbfgs')\n",
    "\n",
    "# Training the individual models on full training data\n",
    "svc_best.fit(X_train_split, y_train_split)\n",
    "lr_best.fit(X_train_split, y_train_split)\n",
    "\n",
    "# Ensemble with Voting Classifier\n",
    "ensemble_model = VotingClassifier(\n",
    "    estimators=[('svc', svc_best), ('lr', lr_best)],\n",
    "    voting='hard'  # Use 'soft' for probabilities\n",
    ")\n",
    "ensemble_model.fit(X_train_split, y_train_split)\n",
    "\n",
    "# Evaluate on validation data\n",
    "ensemble_predictions = ensemble_model.predict(X_val_split)\n",
    "ensemble_accuracy = accuracy_score(y_val_split, ensemble_predictions)\n",
    "\n",
    "print(f\"Ensemble Model Accuracy: {ensemble_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated Ensemble Model Accuracy: 0.5884\n"
     ]
    }
   ],
   "source": [
    "# Update Logistic Regression with a higher max_iter and different solver\n",
    "lr_model_updated = LogisticRegression(C=5, solver='sag', max_iter=100, n_jobs=-1)\n",
    "lr_model_updated.fit(X_train_split, y_train_split)\n",
    "\n",
    "# Re-training ensemble with the updated Logistic Regression model\n",
    "ensemble_model_updated = VotingClassifier(\n",
    "    estimators=[('svc', svc_best), ('lr', lr_model_updated)],\n",
    "    voting='hard'\n",
    ")\n",
    "ensemble_model_updated.fit(X_train_split, y_train_split)\n",
    "\n",
    "# Evaluating the updated ensemble model\n",
    "ensemble_predictions_updated = ensemble_model_updated.predict(X_val_split)\n",
    "ensemble_accuracy_updated = accuracy_score(y_val_split, ensemble_predictions_updated)\n",
    "\n",
    "print(f\"Updated Ensemble Model Accuracy: {ensemble_accuracy_updated:.4f}\")\n",
    "\n",
    "\n",
    "## to get rid of the annoying warnings :( hopefully it worked!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best accuracy on validation set was achieved by Ensemble Model Accuracy: 0.5888 (Linear SVC + Logistic Regression)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make predictions on test data !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = ensemble_model_updated.predict(X_test)\n",
    "y_test_pred_genre = label_encoder.inverse_transform(y_test_pred)\n",
    "test_data['predicted_genre'] = y_test_pred_genre\n",
    "test_data = test_data.drop('Genre', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the trained model to a pickle file\n",
    "with open('artifacts/ensemble_model_updated.pkl', 'wb') as f:\n",
    "    pickle.dump(ensemble_model_updated, f)\n",
    "\n",
    "# Save the label encoder to a pickle file\n",
    "with open('artifacts/label_encoder.pkl', 'wb') as f:\n",
    "    pickle.dump(label_encoder, f)\n",
    "\n",
    "# Save the TF-IDF vectorizer to a pickle file\n",
    "with open('artifacts/tfidf_vectorizer.pkl', 'wb') as f:\n",
    "    pickle.dump(tfidf_vectorizer, f)\n",
    "\n",
    "test_data.to_csv('artifacts/test_data_predictions.csv', index=False)"
   ]
  }
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