Main copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>genre</th>\n",
       "      <th>original_language</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>278</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Drama,Crime</td>\n",
       "      <td>en</td>\n",
       "      <td>Framed in the 1940s for the double murder of h...</td>\n",
       "      <td>94.075</td>\n",
       "      <td>1994-09-23</td>\n",
       "      <td>8.7</td>\n",
       "      <td>21862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19404</td>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>hi</td>\n",
       "      <td>Raj is a rich, carefree, happy-go-lucky second...</td>\n",
       "      <td>25.408</td>\n",
       "      <td>1995-10-19</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>Drama,Crime</td>\n",
       "      <td>en</td>\n",
       "      <td>Spanning the years 1945 to 1955, a chronicle o...</td>\n",
       "      <td>90.585</td>\n",
       "      <td>1972-03-14</td>\n",
       "      <td>8.7</td>\n",
       "      <td>16280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>Drama,History,War</td>\n",
       "      <td>en</td>\n",
       "      <td>The true story of how businessman Oskar Schind...</td>\n",
       "      <td>44.761</td>\n",
       "      <td>1993-12-15</td>\n",
       "      <td>8.6</td>\n",
       "      <td>12959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>Drama,Crime</td>\n",
       "      <td>en</td>\n",
       "      <td>In the continuing saga of the Corleone crime f...</td>\n",
       "      <td>57.749</td>\n",
       "      <td>1974-12-20</td>\n",
       "      <td>8.6</td>\n",
       "      <td>9811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>10196</td>\n",
       "      <td>The Last Airbender</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>en</td>\n",
       "      <td>The story follows the adventures of Aang, a yo...</td>\n",
       "      <td>98.322</td>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>331446</td>\n",
       "      <td>Sharknado 3: Oh Hell No!</td>\n",
       "      <td>Action,TV Movie,Science Fiction,Comedy,Adventure</td>\n",
       "      <td>en</td>\n",
       "      <td>The sharks take bite out of the East Coast whe...</td>\n",
       "      <td>12.490</td>\n",
       "      <td>2015-07-22</td>\n",
       "      <td>4.7</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>13995</td>\n",
       "      <td>Captain America</td>\n",
       "      <td>Action,Science Fiction,War</td>\n",
       "      <td>en</td>\n",
       "      <td>During World War II, a brave, patriotic Americ...</td>\n",
       "      <td>18.333</td>\n",
       "      <td>1990-12-14</td>\n",
       "      <td>4.6</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>2312</td>\n",
       "      <td>In the Name of the King: A Dungeon Siege Tale</td>\n",
       "      <td>Adventure,Fantasy,Action,Drama</td>\n",
       "      <td>en</td>\n",
       "      <td>A man named Farmer sets out to rescue his kidn...</td>\n",
       "      <td>15.159</td>\n",
       "      <td>2007-11-29</td>\n",
       "      <td>4.7</td>\n",
       "      <td>668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>455957</td>\n",
       "      <td>Domino</td>\n",
       "      <td>Thriller,Action,Crime</td>\n",
       "      <td>en</td>\n",
       "      <td>Seeking justice for his partner’s murder by an...</td>\n",
       "      <td>16.482</td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>4.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                          title  \\\n",
       "0        278                       The Shawshank Redemption   \n",
       "1      19404                    Dilwale Dulhania Le Jayenge   \n",
       "2        238                                  The Godfather   \n",
       "3        424                               Schindler's List   \n",
       "4        240                         The Godfather: Part II   \n",
       "...      ...                                            ...   \n",
       "9995   10196                             The Last Airbender   \n",
       "9996  331446                       Sharknado 3: Oh Hell No!   \n",
       "9997   13995                                Captain America   \n",
       "9998    2312  In the Name of the King: A Dungeon Siege Tale   \n",
       "9999  455957                                         Domino   \n",
       "\n",
       "                                                 genre original_language  \\\n",
       "0                                          Drama,Crime                en   \n",
       "1                                 Comedy,Drama,Romance                hi   \n",
       "2                                          Drama,Crime                en   \n",
       "3                                    Drama,History,War                en   \n",
       "4                                          Drama,Crime                en   \n",
       "...                                                ...               ...   \n",
       "9995                          Action,Adventure,Fantasy                en   \n",
       "9996  Action,TV Movie,Science Fiction,Comedy,Adventure                en   \n",
       "9997                        Action,Science Fiction,War                en   \n",
       "9998                    Adventure,Fantasy,Action,Drama                en   \n",
       "9999                             Thriller,Action,Crime                en   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "0     Framed in the 1940s for the double murder of h...      94.075   \n",
       "1     Raj is a rich, carefree, happy-go-lucky second...      25.408   \n",
       "2     Spanning the years 1945 to 1955, a chronicle o...      90.585   \n",
       "3     The true story of how businessman Oskar Schind...      44.761   \n",
       "4     In the continuing saga of the Corleone crime f...      57.749   \n",
       "...                                                 ...         ...   \n",
       "9995  The story follows the adventures of Aang, a yo...      98.322   \n",
       "9996  The sharks take bite out of the East Coast whe...      12.490   \n",
       "9997  During World War II, a brave, patriotic Americ...      18.333   \n",
       "9998  A man named Farmer sets out to rescue his kidn...      15.159   \n",
       "9999  Seeking justice for his partner’s murder by an...      16.482   \n",
       "\n",
       "     release_date  vote_average  vote_count  \n",
       "0      1994-09-23           8.7       21862  \n",
       "1      1995-10-19           8.7        3731  \n",
       "2      1972-03-14           8.7       16280  \n",
       "3      1993-12-15           8.6       12959  \n",
       "4      1974-12-20           8.6        9811  \n",
       "...           ...           ...         ...  \n",
       "9995   2010-06-30           4.7        3347  \n",
       "9996   2015-07-22           4.7         417  \n",
       "9997   1990-12-14           4.6         332  \n",
       "9998   2007-11-29           4.7         668  \n",
       "9999   2019-05-31           4.6         221  \n",
       "\n",
       "[10000 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('dataset.csv')\n",
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>161243.505000</td>\n",
       "      <td>34.697267</td>\n",
       "      <td>6.621150</td>\n",
       "      <td>1547.309400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211422.046043</td>\n",
       "      <td>211.684175</td>\n",
       "      <td>0.766231</td>\n",
       "      <td>2648.295789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10127.750000</td>\n",
       "      <td>9.154750</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>315.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30002.500000</td>\n",
       "      <td>13.637500</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>583.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>310133.500000</td>\n",
       "      <td>25.651250</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>934761.000000</td>\n",
       "      <td>10436.917000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>31917.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity  vote_average    vote_count\n",
       "count   10000.000000  10000.000000  10000.000000  10000.000000\n",
       "mean   161243.505000     34.697267      6.621150   1547.309400\n",
       "std    211422.046043    211.684175      0.766231   2648.295789\n",
       "min         5.000000      0.600000      4.600000    200.000000\n",
       "25%     10127.750000      9.154750      6.100000    315.000000\n",
       "50%     30002.500000     13.637500      6.600000    583.500000\n",
       "75%    310133.500000     25.651250      7.200000   1460.000000\n",
       "max    934761.000000  10436.917000      8.700000  31917.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 10000 non-null  int64  \n",
      " 1   title              10000 non-null  object \n",
      " 2   genre              9997 non-null   object \n",
      " 3   original_language  10000 non-null  object \n",
      " 4   overview           9987 non-null   object \n",
      " 5   popularity         10000 non-null  float64\n",
      " 6   release_date       10000 non-null  object \n",
      " 7   vote_average       10000 non-null  float64\n",
      " 8   vote_count         10000 non-null  int64  \n",
      "dtypes: float64(2), int64(2), object(5)\n",
      "memory usage: 703.3+ KB\n"
     ]
    }
   ],
   "source": [
    "movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    0\n",
       "title                 0\n",
       "genre                 3\n",
       "original_language     0\n",
       "overview             13\n",
       "popularity            0\n",
       "release_date          0\n",
       "vote_average          0\n",
       "vote_count            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Selection Part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'genre', 'original_language', 'overview', 'popularity',\n",
       "       'release_date', 'vote_average', 'vote_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>278</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Drama,Crime</td>\n",
       "      <td>Framed in the 1940s for the double murder of h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19404</td>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>Raj is a rich, carefree, happy-go-lucky second...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>Drama,Crime</td>\n",
       "      <td>Spanning the years 1945 to 1955, a chronicle o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>Drama,History,War</td>\n",
       "      <td>The true story of how businessman Oskar Schind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>Drama,Crime</td>\n",
       "      <td>In the continuing saga of the Corleone crime f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>10196</td>\n",
       "      <td>The Last Airbender</td>\n",
       "      <td>Action,Adventure,Fantasy</td>\n",
       "      <td>The story follows the adventures of Aang, a yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>331446</td>\n",
       "      <td>Sharknado 3: Oh Hell No!</td>\n",
       "      <td>Action,TV Movie,Science Fiction,Comedy,Adventure</td>\n",
       "      <td>The sharks take bite out of the East Coast whe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>13995</td>\n",
       "      <td>Captain America</td>\n",
       "      <td>Action,Science Fiction,War</td>\n",
       "      <td>During World War II, a brave, patriotic Americ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>2312</td>\n",
       "      <td>In the Name of the King: A Dungeon Siege Tale</td>\n",
       "      <td>Adventure,Fantasy,Action,Drama</td>\n",
       "      <td>A man named Farmer sets out to rescue his kidn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>455957</td>\n",
       "      <td>Domino</td>\n",
       "      <td>Thriller,Action,Crime</td>\n",
       "      <td>Seeking justice for his partner’s murder by an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                          title  \\\n",
       "0        278                       The Shawshank Redemption   \n",
       "1      19404                    Dilwale Dulhania Le Jayenge   \n",
       "2        238                                  The Godfather   \n",
       "3        424                               Schindler's List   \n",
       "4        240                         The Godfather: Part II   \n",
       "...      ...                                            ...   \n",
       "9995   10196                             The Last Airbender   \n",
       "9996  331446                       Sharknado 3: Oh Hell No!   \n",
       "9997   13995                                Captain America   \n",
       "9998    2312  In the Name of the King: A Dungeon Siege Tale   \n",
       "9999  455957                                         Domino   \n",
       "\n",
       "                                                 genre  \\\n",
       "0                                          Drama,Crime   \n",
       "1                                 Comedy,Drama,Romance   \n",
       "2                                          Drama,Crime   \n",
       "3                                    Drama,History,War   \n",
       "4                                          Drama,Crime   \n",
       "...                                                ...   \n",
       "9995                          Action,Adventure,Fantasy   \n",
       "9996  Action,TV Movie,Science Fiction,Comedy,Adventure   \n",
       "9997                        Action,Science Fiction,War   \n",
       "9998                    Adventure,Fantasy,Action,Drama   \n",
       "9999                             Thriller,Action,Crime   \n",
       "\n",
       "                                               overview  \n",
       "0     Framed in the 1940s for the double murder of h...  \n",
       "1     Raj is a rich, carefree, happy-go-lucky second...  \n",
       "2     Spanning the years 1945 to 1955, a chronicle o...  \n",
       "3     The true story of how businessman Oskar Schind...  \n",
       "4     In the continuing saga of the Corleone crime f...  \n",
       "...                                                 ...  \n",
       "9995  The story follows the adventures of Aang, a yo...  \n",
       "9996  The sharks take bite out of the East Coast whe...  \n",
       "9997  During World War II, a brave, patriotic Americ...  \n",
       "9998  A man named Farmer sets out to rescue his kidn...  \n",
       "9999  Seeking justice for his partner’s murder by an...  \n",
       "\n",
       "[10000 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies1 = movies[['id', 'title', 'genre', 'overview']]\n",
    "movies1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMIN\\AppData\\Local\\Temp\\ipykernel_23104\\4291272766.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  movies1['tag']=movies1['overview']+movies1['genre']\n"
     ]
    }
   ],
   "source": [
    "movies1['tag']=movies1['overview']+movies1['genre']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = movies1.drop(columns=['overview','genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>278</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Framed in the 1940s for the double murder of h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19404</td>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>Raj is a rich, carefree, happy-go-lucky second...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>Spanning the years 1945 to 1955, a chronicle o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>424</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>The true story of how businessman Oskar Schind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>240</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>In the continuing saga of the Corleone crime f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>10196</td>\n",
       "      <td>The Last Airbender</td>\n",
       "      <td>The story follows the adventures of Aang, a yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>331446</td>\n",
       "      <td>Sharknado 3: Oh Hell No!</td>\n",
       "      <td>The sharks take bite out of the East Coast whe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>13995</td>\n",
       "      <td>Captain America</td>\n",
       "      <td>During World War II, a brave, patriotic Americ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>2312</td>\n",
       "      <td>In the Name of the King: A Dungeon Siege Tale</td>\n",
       "      <td>A man named Farmer sets out to rescue his kidn...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>455957</td>\n",
       "      <td>Domino</td>\n",
       "      <td>Seeking justice for his partner’s murder by an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                          title  \\\n",
       "0        278                       The Shawshank Redemption   \n",
       "1      19404                    Dilwale Dulhania Le Jayenge   \n",
       "2        238                                  The Godfather   \n",
       "3        424                               Schindler's List   \n",
       "4        240                         The Godfather: Part II   \n",
       "...      ...                                            ...   \n",
       "9995   10196                             The Last Airbender   \n",
       "9996  331446                       Sharknado 3: Oh Hell No!   \n",
       "9997   13995                                Captain America   \n",
       "9998    2312  In the Name of the King: A Dungeon Siege Tale   \n",
       "9999  455957                                         Domino   \n",
       "\n",
       "                                                    tag  \n",
       "0     Framed in the 1940s for the double murder of h...  \n",
       "1     Raj is a rich, carefree, happy-go-lucky second...  \n",
       "2     Spanning the years 1945 to 1955, a chronicle o...  \n",
       "3     The true story of how businessman Oskar Schind...  \n",
       "4     In the continuing saga of the Corleone crime f...  \n",
       "...                                                 ...  \n",
       "9995  The story follows the adventures of Aang, a yo...  \n",
       "9996  The sharks take bite out of the East Coast whe...  \n",
       "9997  During World War II, a brave, patriotic Americ...  \n",
       "9998  A man named Farmer sets out to rescue his kidn...  \n",
       "9999  Seeking justice for his partner’s murder by an...  \n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Bag of word\n",
    "2. TFIDF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cv=CountVectorizer(max_features=10000, stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(max_features=10000, stop_words=&#x27;english&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_features=10000, stop_words=&#x27;english&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "CountVectorizer(max_features=10000, stop_words='english')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = cv.fit_transform(movies['overview'].values.astype('U')).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10000)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.03030303, 0.02861819, ..., 0.0836242 , 0.08444007,\n",
       "        0.03481553],\n",
       "       [0.03030303, 1.        , 0.05723638, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.02861819, 0.05723638, 1.        , ..., 0.02632491, 0.03987261,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.0836242 , 0.        , 0.02632491, ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.08444007, 0.        , 0.03987261, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.03481553, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "similarrity = cosine_similarity(vector)\n",
    "similarrity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[new_data['title']=='Once Upon a Time in America'].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Godfather\n",
      "The Godfather: Part II\n",
      "Kind Hearts and Coronets\n",
      "Sinister\n",
      "Still Walking\n"
     ]
    }
   ],
   "source": [
    "distance = sorted(list(enumerate(similarrity[2])),reverse=True,key=lambda vector:vector[1])\n",
    "for i in distance[0:5]:\n",
    "  print(movies.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recomand(movie):\n",
    "  index = movies[movies['title']==movie].index[0]\n",
    "  distance = sorted(list(enumerate(similarrity[index])),reverse=True,key=lambda vector:vector[1])\n",
    "  for i in distance[0:5]:\n",
    "    print(movies.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iron Man\n",
      "Clown\n",
      "Iron Man 2\n",
      "The Wizards Return: Alex vs. Alex\n",
      "G.O.R.A.\n"
     ]
    }
   ],
   "source": [
    "recomand('Iron Man')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3066</th>\n",
       "      <td>507086</td>\n",
       "      <td>Jurassic World Dominion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>438148</td>\n",
       "      <td>Minions: The Rise of Gru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>616037</td>\n",
       "      <td>Thor: Love and Thunder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>361743</td>\n",
       "      <td>Top Gun: Maverick</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>756999</td>\n",
       "      <td>The Black Phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>718789</td>\n",
       "      <td>Lightyear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>453395</td>\n",
       "      <td>Doctor Strange in the Multiverse of Madness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>759175</td>\n",
       "      <td>The Princess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>836225</td>\n",
       "      <td>The Exorcism of God</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>560057</td>\n",
       "      <td>The Sea Beast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>634649</td>\n",
       "      <td>Spider-Man: No Way Home</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>675353</td>\n",
       "      <td>Sonic the Hedgehog 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>626735</td>\n",
       "      <td>Dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>810693</td>\n",
       "      <td>Jujutsu Kaisen 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4025</th>\n",
       "      <td>338953</td>\n",
       "      <td>Fantastic Beasts: The Secrets of Dumbledore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                                        title\n",
       "3066  507086                      Jurassic World Dominion\n",
       "1337  438148                     Minions: The Rise of Gru\n",
       "4522  616037                       Thor: Love and Thunder\n",
       "61    361743                            Top Gun: Maverick\n",
       "388   756999                              The Black Phone\n",
       "1990  718789                                    Lightyear\n",
       "1313  453395  Doctor Strange in the Multiverse of Madness\n",
       "2815  759175                                 The Princess\n",
       "3103  836225                          The Exorcism of God\n",
       "948   560057                                The Sea Beast\n",
       "258   634649                      Spider-Man: No Way Home\n",
       "837   675353                         Sonic the Hedgehog 2\n",
       "1574  626735                                          Dog\n",
       "556   810693                             Jujutsu Kaisen 0\n",
       "4025  338953  Fantastic Beasts: The Secrets of Dumbledore"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top15_popular = movies.sort_values(by='popularity',ascending=False).head(15)\n",
    "top15_popular = top15_popular[['id','title']]\n",
    "top15_popular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.03030303, 0.02861819, ..., 0.0836242 , 0.08444007,\n",
       "        0.03481553],\n",
       "       [0.03030303, 1.        , 0.05723638, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.02861819, 0.05723638, 1.        , ..., 0.02632491, 0.03987261,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.0836242 , 0.        , 0.02632491, ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.08444007, 0.        , 0.03987261, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.03481553, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "pickle.dump(movies, open('movie_list.pkl','wb'))\n",
    "pickle.dump(similarrity,open('similarrity.pkl','wb'))\n",
    "pickle.load(open('movie_list.pkl','rb'))\n",
    "pickle.load(open('similarrity.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "top15_popular.to_csv('./top15_popular.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.03030303, 0.02861819, ..., 0.0836242 , 0.08444007,\n",
       "        0.03481553],\n",
       "       [0.03030303, 1.        , 0.05723638, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.02861819, 0.05723638, 1.        , ..., 0.02632491, 0.03987261,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.0836242 , 0.        , 0.02632491, ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.08444007, 0.        , 0.03987261, ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.03481553, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.save(\"similarity\", similarrity)\n",
    "np.load(\"similarity.npy\")"
   ]
  }
 ],
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