.ipynb_checkpoints/Classification using Naive Bayes-checkpoint.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial Event Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [\"This was an awesome movie\",\n",
    "     \"Great movie! I liked it a lot\",\n",
    "     \"Happy Ending! awesome acting by the hero\",\n",
    "     \"loved it! truly great\",\n",
    "     \"bad not upto the mark\",\n",
    "     \"could have better\",\n",
    "     \"Disappointing movie\"]\n",
    "\n",
    "y = [1,1,1,1,0,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'clean_text'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m--------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-dc0307cad964>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mclean_text\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'clean_text'"
     ]
    }
   ],
   "source": [
    "import clean_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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