movie-recommender-systems.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "da447453-96e3-48fe-93cd-b9195433add6",
    "_uuid": "b63091dc0b9d04f19ab8861754a83b79c1789e96"
   },
   "source": [
    "# Movies Recommender System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "78fea705-f9ad-4a81-aafd-8b7b624110d1",
    "_uuid": "1c291fe3725c706e0003f01f0102abb9709f25fc"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from ast import literal_eval\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel, cosine_similarity\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet\n",
    "from surprise import Reader, Dataset, SVD\n",
    "\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "0356d2a0-d8b2-4234-af78-bd51eaa3a390",
    "_uuid": "e0d62612bc9b9e9561afdc48fd1e75a9b0c82337"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</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>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\n",
       "0                    Toy Story   \n",
       "1                      Jumanji   \n",
       "2             Grumpier Old Men   \n",
       "3            Waiting to Exhale   \n",
       "4  Father of the Bride Part II   \n",
       "\n",
       "                                            overview    ...     release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...    ...       1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...    ...       1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...    ...       1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...    ...       1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...    ...       1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
       "0  373554033.0    81.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "2          0.0   101.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md = pd. read_csv('movies_metadata.csv')\n",
    "md.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "f933a861-409a-4df7-98f1-7960a2a3874b",
    "_uuid": "d4df4c2158595611f2ea7ad4b16f71900e529ea8"
   },
   "outputs": [],
   "source": [
    "md['genres'] = md['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "de5d2a9a-9d67-4309-9256-844756318bd0",
    "_uuid": "1e732487caba09d9543f3da49cbe7ff5092e2675"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.244896612406511"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vote_counts = md[md['vote_count'].notnull()]['vote_count'].astype('int')\n",
    "vote_averages = md[md['vote_average'].notnull()]['vote_average'].astype('int')\n",
    "C = vote_averages.mean()\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "f2a3dff7-6430-471b-bd83-589779ab52b0",
    "_uuid": "23c2e113380f1bad9205ab2d0f8c372b0c246e79"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "434.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = vote_counts.quantile(0.95)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "93f480df-6e7b-427c-bbc0-906fd41640b0",
    "_uuid": "dbb8261dedb84e4a8ea8bed843c9de3ad9f3859d"
   },
   "outputs": [],
   "source": [
    "md['year'] = pd.to_datetime(md['release_date'], errors='coerce').apply(lambda x: str(x).split('-')[0] if x != np.nan else np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "e7e91af4-7ff5-4553-bfbb-b9339096397e",
    "_uuid": "dcb30bee69ef1a6ead2c745565e5e7bfd5314d7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2274, 6)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qualified = md[(md['vote_count'] >= m) & (md['vote_count'].notnull()) & (md['vote_average'].notnull())][['title', 'year', 'vote_count', 'vote_average', 'popularity', 'genres']]\n",
    "qualified['vote_count'] = qualified['vote_count'].astype('int')\n",
    "qualified['vote_average'] = qualified['vote_average'].astype('int')\n",
    "qualified.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "db959c40-7618-4755-a05a-5347f9460929",
    "_uuid": "6414e1aa7ed8cb4184ec451f02a18cd187e5345e"
   },
   "outputs": [],
   "source": [
    "def weighted_rating(x):\n",
    "    v = x['vote_count']\n",
    "    R = x['vote_average']\n",
    "    return (v/(v+m) * R) + (m/(m+v) * C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "0f5e4384-51be-41df-a023-12a13e77d66c",
    "_uuid": "78ec5052b0e89ae51a834aa3a305cf55b6849f42"
   },
   "outputs": [],
   "source": [
    "qualified['wr'] = qualified.apply(weighted_rating, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "ccbd8eca-cbfe-4843-9885-de11976f117c",
    "_uuid": "ed0ac7c8483f904d4f5ec0d554096d4782fa8bcf"
   },
   "outputs": [],
   "source": [
    "qualified = qualified.sort_values('wr', ascending=False).head(250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "36a1b9ac-aff3-4d80-9e19-83a45f282062",
    "_uuid": "75eb9e1de05386d229746785a25c6a5c6ee4b61a"
   },
   "source": [
    "### Top Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "0bca5b03-0835-4902-aaa1-beceb9d9d47f",
    "_uuid": "f7f6821b7e9aff099b81fcc425fd20109894b351"
   },
   "outputs": [
    {
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       "\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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>genres</th>\n",
       "      <th>wr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15480</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>14075</td>\n",
       "      <td>8</td>\n",
       "      <td>29.1081</td>\n",
       "      <td>[Action, Thriller, Science Fiction, Mystery, A...</td>\n",
       "      <td>7.917588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>12269</td>\n",
       "      <td>8</td>\n",
       "      <td>123.167</td>\n",
       "      <td>[Drama, Action, Crime, Thriller]</td>\n",
       "      <td>7.905871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22879</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>2014</td>\n",
       "      <td>11187</td>\n",
       "      <td>8</td>\n",
       "      <td>32.2135</td>\n",
       "      <td>[Adventure, Drama, Science Fiction]</td>\n",
       "      <td>7.897107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>Fight Club</td>\n",
       "      <td>1999</td>\n",
       "      <td>9678</td>\n",
       "      <td>8</td>\n",
       "      <td>63.8696</td>\n",
       "      <td>[Drama]</td>\n",
       "      <td>7.881753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4863</th>\n",
       "      <td>The Lord of the Rings: The Fellowship of the Ring</td>\n",
       "      <td>2001</td>\n",
       "      <td>8892</td>\n",
       "      <td>8</td>\n",
       "      <td>32.0707</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>7.871787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>1994</td>\n",
       "      <td>8670</td>\n",
       "      <td>8</td>\n",
       "      <td>140.95</td>\n",
       "      <td>[Thriller, Crime]</td>\n",
       "      <td>7.868660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>8358</td>\n",
       "      <td>8</td>\n",
       "      <td>51.6454</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>7.864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000</th>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>8226</td>\n",
       "      <td>8</td>\n",
       "      <td>29.3244</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>7.861927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>1994</td>\n",
       "      <td>8147</td>\n",
       "      <td>8</td>\n",
       "      <td>48.3072</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>7.860656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5814</th>\n",
       "      <td>The Lord of the Rings: The Two Towers</td>\n",
       "      <td>2002</td>\n",
       "      <td>7641</td>\n",
       "      <td>8</td>\n",
       "      <td>29.4235</td>\n",
       "      <td>[Adventure, Fantasy, Action]</td>\n",
       "      <td>7.851924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Star Wars</td>\n",
       "      <td>1977</td>\n",
       "      <td>6778</td>\n",
       "      <td>8</td>\n",
       "      <td>42.1497</td>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>7.834205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>Back to the Future</td>\n",
       "      <td>1985</td>\n",
       "      <td>6239</td>\n",
       "      <td>8</td>\n",
       "      <td>25.7785</td>\n",
       "      <td>[Adventure, Comedy, Science Fiction, Family]</td>\n",
       "      <td>7.820813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>6024</td>\n",
       "      <td>8</td>\n",
       "      <td>41.1093</td>\n",
       "      <td>[Drama, Crime]</td>\n",
       "      <td>7.814847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>The Empire Strikes Back</td>\n",
       "      <td>1980</td>\n",
       "      <td>5998</td>\n",
       "      <td>8</td>\n",
       "      <td>19.471</td>\n",
       "      <td>[Adventure, Action, Science Fiction]</td>\n",
       "      <td>7.814099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Se7en</td>\n",
       "      <td>1995</td>\n",
       "      <td>5915</td>\n",
       "      <td>8</td>\n",
       "      <td>18.4574</td>\n",
       "      <td>[Crime, Mystery, Thriller]</td>\n",
       "      <td>7.811669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  year  vote_count  \\\n",
       "15480                                          Inception  2010       14075   \n",
       "12481                                    The Dark Knight  2008       12269   \n",
       "22879                                       Interstellar  2014       11187   \n",
       "2843                                          Fight Club  1999        9678   \n",
       "4863   The Lord of the Rings: The Fellowship of the Ring  2001        8892   \n",
       "292                                         Pulp Fiction  1994        8670   \n",
       "314                             The Shawshank Redemption  1994        8358   \n",
       "7000       The Lord of the Rings: The Return of the King  2003        8226   \n",
       "351                                         Forrest Gump  1994        8147   \n",
       "5814               The Lord of the Rings: The Two Towers  2002        7641   \n",
       "256                                            Star Wars  1977        6778   \n",
       "1225                                  Back to the Future  1985        6239   \n",
       "834                                        The Godfather  1972        6024   \n",
       "1154                             The Empire Strikes Back  1980        5998   \n",
       "46                                                 Se7en  1995        5915   \n",
       "\n",
       "       vote_average popularity  \\\n",
       "15480             8    29.1081   \n",
       "12481             8    123.167   \n",
       "22879             8    32.2135   \n",
       "2843              8    63.8696   \n",
       "4863              8    32.0707   \n",
       "292               8     140.95   \n",
       "314               8    51.6454   \n",
       "7000              8    29.3244   \n",
       "351               8    48.3072   \n",
       "5814              8    29.4235   \n",
       "256               8    42.1497   \n",
       "1225              8    25.7785   \n",
       "834               8    41.1093   \n",
       "1154              8     19.471   \n",
       "46                8    18.4574   \n",
       "\n",
       "                                                  genres        wr  \n",
       "15480  [Action, Thriller, Science Fiction, Mystery, A...  7.917588  \n",
       "12481                   [Drama, Action, Crime, Thriller]  7.905871  \n",
       "22879                [Adventure, Drama, Science Fiction]  7.897107  \n",
       "2843                                             [Drama]  7.881753  \n",
       "4863                        [Adventure, Fantasy, Action]  7.871787  \n",
       "292                                    [Thriller, Crime]  7.868660  \n",
       "314                                       [Drama, Crime]  7.864000  \n",
       "7000                        [Adventure, Fantasy, Action]  7.861927  \n",
       "351                             [Comedy, Drama, Romance]  7.860656  \n",
       "5814                        [Adventure, Fantasy, Action]  7.851924  \n",
       "256                 [Adventure, Action, Science Fiction]  7.834205  \n",
       "1225        [Adventure, Comedy, Science Fiction, Family]  7.820813  \n",
       "834                                       [Drama, Crime]  7.814847  \n",
       "1154                [Adventure, Action, Science Fiction]  7.814099  \n",
       "46                            [Crime, Mystery, Thriller]  7.811669  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qualified.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "c6be871d-62d4-4ec4-abe4-6ca1b1c01fa0",
    "_uuid": "b59a35544efcd50e89e4bb1ea14bbfe32fc9bcd3"
   },
   "outputs": [],
   "source": [
    "s = md.apply(lambda x: pd.Series(x['genres']),axis=1).stack().reset_index(level=1, drop=True)\n",
    "s.name = 'genre'\n",
    "gen_md = md.drop('genres', axis=1).join(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "759652cb-5b16-4951-995c-6a3bdbae0e4e",
    "_uuid": "1fdfea9cd4b046b11ff8f2ac4f2bea74fccb8a8b"
   },
   "outputs": [],
   "source": [
    "def build_chart(genre, percentile=0.85):\n",
    "    df = gen_md[gen_md['genre'] == genre]\n",
    "    vote_counts = df[df['vote_count'].notnull()]['vote_count'].astype('int')\n",
    "    vote_averages = df[df['vote_average'].notnull()]['vote_average'].astype('int')\n",
    "    C = vote_averages.mean()\n",
    "    m = vote_counts.quantile(percentile)\n",
    "    \n",
    "    qualified = df[(df['vote_count'] >= m) & (df['vote_count'].notnull()) & (df['vote_average'].notnull())][['title', 'year', 'vote_count', 'vote_average', 'popularity']]\n",
    "    qualified['vote_count'] = qualified['vote_count'].astype('int')\n",
    "    qualified['vote_average'] = qualified['vote_average'].astype('int')\n",
    "    \n",
    "    qualified['wr'] = qualified.apply(lambda x: (x['vote_count']/(x['vote_count']+m) * x['vote_average']) + (m/(m+x['vote_count']) * C), axis=1)\n",
    "    qualified = qualified.sort_values('wr', ascending=False).head(250)\n",
    "    \n",
    "    return qualified"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "55f22de0-0df9-405a-9197-df5a8c0e8466",
    "_uuid": "a76ce0f8a8f614aa6f23cb9a6e0fd6af6d048642"
   },
   "source": [
    "\n",
    "### Top Romance Movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "af61da24-494b-48ac-b7b7-64ec0eaedaf8",
    "_uuid": "bf2816495a16de1bb84dc826fd33eee7e663f45d"
   },
   "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>title</th>\n",
       "      <th>year</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>wr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10309</th>\n",
       "      <td>Dilwale Dulhania Le Jayenge</td>\n",
       "      <td>1995</td>\n",
       "      <td>661</td>\n",
       "      <td>9</td>\n",
       "      <td>34.457</td>\n",
       "      <td>8.565285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>1994</td>\n",
       "      <td>8147</td>\n",
       "      <td>8</td>\n",
       "      <td>48.3072</td>\n",
       "      <td>7.971357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>Vertigo</td>\n",
       "      <td>1958</td>\n",
       "      <td>1162</td>\n",
       "      <td>8</td>\n",
       "      <td>18.2082</td>\n",
       "      <td>7.811667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40251</th>\n",
       "      <td>Your Name.</td>\n",
       "      <td>2016</td>\n",
       "      <td>1030</td>\n",
       "      <td>8</td>\n",
       "      <td>34.461252</td>\n",
       "      <td>7.789489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>Some Like It Hot</td>\n",
       "      <td>1959</td>\n",
       "      <td>835</td>\n",
       "      <td>8</td>\n",
       "      <td>11.8451</td>\n",
       "      <td>7.745154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>Cinema Paradiso</td>\n",
       "      <td>1988</td>\n",
       "      <td>834</td>\n",
       "      <td>8</td>\n",
       "      <td>14.177</td>\n",
       "      <td>7.744878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19901</th>\n",
       "      <td>Paperman</td>\n",
       "      <td>2012</td>\n",
       "      <td>734</td>\n",
       "      <td>8</td>\n",
       "      <td>7.19863</td>\n",
       "      <td>7.713951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37863</th>\n",
       "      <td>Sing Street</td>\n",
       "      <td>2016</td>\n",
       "      <td>669</td>\n",
       "      <td>8</td>\n",
       "      <td>10.672862</td>\n",
       "      <td>7.689483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>The Apartment</td>\n",
       "      <td>1960</td>\n",
       "      <td>498</td>\n",
       "      <td>8</td>\n",
       "      <td>11.9943</td>\n",
       "      <td>7.599317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38718</th>\n",
       "      <td>The Handmaiden</td>\n",
       "      <td>2016</td>\n",
       "      <td>453</td>\n",
       "      <td>8</td>\n",
       "      <td>16.727405</td>\n",
       "      <td>7.566166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>City Lights</td>\n",
       "      <td>1931</td>\n",
       "      <td>444</td>\n",
       "      <td>8</td>\n",
       "      <td>10.8915</td>\n",
       "      <td>7.558867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24886</th>\n",
       "      <td>The Way He Looks</td>\n",
       "      <td>2014</td>\n",
       "      <td>262</td>\n",
       "      <td>8</td>\n",
       "      <td>5.71127</td>\n",
       "      <td>7.331363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45437</th>\n",
       "      <td>In a Heartbeat</td>\n",
       "      <td>2017</td>\n",
       "      <td>146</td>\n",
       "      <td>8</td>\n",
       "      <td>20.82178</td>\n",
       "      <td>7.003959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>1997</td>\n",
       "      <td>7770</td>\n",
       "      <td>7</td>\n",
       "      <td>26.8891</td>\n",
       "      <td>6.981546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19731</th>\n",
       "      <td>Silver Linings Playbook</td>\n",
       "      <td>2012</td>\n",
       "      <td>4840</td>\n",
       "      <td>7</td>\n",
       "      <td>14.4881</td>\n",
       "      <td>6.970581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             title  year  vote_count  vote_average popularity  \\\n",
       "10309  Dilwale Dulhania Le Jayenge  1995         661             9     34.457   \n",
       "351                   Forrest Gump  1994        8147             8    48.3072   \n",
       "876                        Vertigo  1958        1162             8    18.2082   \n",
       "40251                   Your Name.  2016        1030             8  34.461252   \n",
       "883               Some Like It Hot  1959         835             8    11.8451   \n",
       "1132               Cinema Paradiso  1988         834             8     14.177   \n",
       "19901                     Paperman  2012         734             8    7.19863   \n",
       "37863                  Sing Street  2016         669             8  10.672862   \n",
       "882                  The Apartment  1960         498             8    11.9943   \n",
       "38718               The Handmaiden  2016         453             8  16.727405   \n",
       "3189                   City Lights  1931         444             8    10.8915   \n",
       "24886             The Way He Looks  2014         262             8    5.71127   \n",
       "45437               In a Heartbeat  2017         146             8   20.82178   \n",
       "1639                       Titanic  1997        7770             7    26.8891   \n",
       "19731      Silver Linings Playbook  2012        4840             7    14.4881   \n",
       "\n",
       "             wr  \n",
       "10309  8.565285  \n",
       "351    7.971357  \n",
       "876    7.811667  \n",
       "40251  7.789489  \n",
       "883    7.745154  \n",
       "1132   7.744878  \n",
       "19901  7.713951  \n",
       "37863  7.689483  \n",
       "882    7.599317  \n",
       "38718  7.566166  \n",
       "3189   7.558867  \n",
       "24886  7.331363  \n",
       "45437  7.003959  \n",
       "1639   6.981546  \n",
       "19731  6.970581  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_chart('Romance').head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b6e1eb8b-6109-40ff-ab8a-37bd047c03ac",
    "_uuid": "0b0f95bdf966d06c2d7053535d46183b03a75cc1"
   },
   "source": [
    "## Content Based Recommender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "bd5f4440-f90d-46a4-8640-204541d99d40",
    "_uuid": "dc237e14c8346bdd51624d21b6b082399afd3062"
   },
   "outputs": [],
   "source": [
    "links_small = pd.read_csv('links_small.csv')\n",
    "links_small = links_small[links_small['tmdbId'].notnull()]['tmdbId'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "cda91291-c8d5-475a-8b0f-ab4030d29f62",
    "_uuid": "6c5af69fefa991d46594401a5eac0656e5980484"
   },
   "outputs": [],
   "source": [
    "md = md.drop([19730, 29503, 35587])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "f68cb861-7c30-4b3d-b655-520379e61d70",
    "_uuid": "87eb84540a3c22a3f7bf54edeee5b491ef571416"
   },
   "outputs": [],
   "source": [
    "#Check EDA Notebook for how and why I got these indices.\n",
    "md['id'] = md['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "0b968a6a-6b0d-4df6-9e28-91c06e6c36c2",
    "_uuid": "3f2a7f55a030beeb06b713f854e4ca6a69b5ca13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9099, 25)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smd = md[md['id'].isin(links_small)]\n",
    "smd.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1e73645a-0b0b-441f-93b4-ce92069e1852",
    "_uuid": "c41800b7a9d8b8d48ba1c22b8430929f54e9884e"
   },
   "source": [
    "### Movie Description Based Recommender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "c880adf9-051b-40cd-8e95-cd8c4acabb17",
    "_uuid": "453bdc941ba5a1e86e17b0f106dc44040c0defdf"
   },
   "outputs": [],
   "source": [
    "smd['tagline'] = smd['tagline'].fillna('')\n",
    "smd['description'] = smd['overview'] + smd['tagline']\n",
    "smd['description'] = smd['description'].fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "6b385623-a978-43c4-b66a-813d5e9c6790",
    "_uuid": "f9d99726d9f2667fa6fc15ce5c5e31d36a970828"
   },
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n",
    "tfidf_matrix = tf.fit_transform(smd['description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "5e18427c-2ef8-4a98-84e2-323a4af72ade",
    "_uuid": "6c21cc05d3c13ea637028f2e5836c719986e47c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9099, 268124)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7d4db389-9f30-46e9-b8af-69fe9b40ada5",
    "_uuid": "1a98e05af67de3475999e1891d965ce9f73ce316"
   },
   "source": [
    "#### Cosine Similarity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "8b27b34f-a585-42bf-b35f-1de4e9181fce",
    "_uuid": "e324c0e951287359f7810a3da85631ebc7725c53"
   },
   "outputs": [],
   "source": [
    "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "148a994f-3e7f-4350-9ea3-41eb95f106e8",
    "_uuid": "ebb6823a9dce997d965dc89049a06512cbfb17af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.00680476, 0.        , ..., 0.        , 0.00344913,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cosine_sim[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "446d22fd-e7c1-42a6-851b-593f60175ce0",
    "_uuid": "ff016d61895e99fe7b0495c51b86917bdbe2712d"
   },
   "outputs": [],
   "source": [
    "smd = smd.reset_index()\n",
    "titles = smd['title']\n",
    "indices = pd.Series(smd.index, index=smd['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "d7352ae7-fbc3-4459-b257-76a44000ce6a",
    "_uuid": "62f49212a8b935ef63a3643e1bb6f629521f05b7"
   },
   "outputs": [],
   "source": [
    "def get_recommendations(title):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:31]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    return titles.iloc[movie_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "73bb1266-5797-47ce-b61f-fa9e0d7ce077",
    "_uuid": "46f418fd12b35e86a7a2026ef1975be822cadde3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "973      The Godfather: Part II\n",
       "8387                 The Family\n",
       "3509                       Made\n",
       "4196         Johnny Dangerously\n",
       "29               Shanghai Triad\n",
       "5667                       Fury\n",
       "2412             American Movie\n",
       "1582    The Godfather: Part III\n",
       "4221                    8 Women\n",
       "2159              Summer of Sam\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Godfather').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "d074112e-79ea-4188-a1e0-addd9bed3c91",
    "_uuid": "f92e310b0225b4a1b8b46e8a87fe2f2b82c158ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7931                      The Dark Knight Rises\n",
       "132                              Batman Forever\n",
       "1113                             Batman Returns\n",
       "8227    Batman: The Dark Knight Returns, Part 2\n",
       "7565                 Batman: Under the Red Hood\n",
       "524                                      Batman\n",
       "7901                           Batman: Year One\n",
       "2579               Batman: Mask of the Phantasm\n",
       "2696                                        JFK\n",
       "8165    Batman: The Dark Knight Returns, Part 1\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Dark Knight').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3a0dd8ba-a580-4c12-95eb-ca04e2191b35",
    "_uuid": "4089c7bcdf984e596e80d2fc6c2a74fe8c05a41f"
   },
   "source": [
    "### Metadata Based Recommender\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "85d2d5e0-165a-4ad5-860d-be1d1aa20c59",
    "_uuid": "aa399813ff512b0d7671e9bfcebf7f41ee139275"
   },
   "outputs": [],
   "source": [
    "credits = pd.read_csv('credits.csv')\n",
    "keywords = pd.read_csv('keywords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "5dc74705-17ee-41b8-932b-dc612c9426c5",
    "_uuid": "e82eb38c3ae53d500ad335943b4bd30a19f22d1a"
   },
   "outputs": [],
   "source": [
    "keywords['id'] = keywords['id'].astype('int')\n",
    "credits['id'] = credits['id'].astype('int')\n",
    "md['id'] = md['id'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "a120fca0-7236-4b97-97f8-970363ed02ed",
    "_uuid": "2f9fc363f77649b3ee41132e0b45f366726a25cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45463, 25)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "39fbbbcd-a0d9-4ad3-9187-e6e2bdb4358e",
    "_uuid": "4b1ebc18f31eb017c6f4f173ab270807c73c002a"
   },
   "outputs": [],
   "source": [
    "md = md.merge(credits, on='id')\n",
    "md = md.merge(keywords, on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "ecdbd741-d8b1-4083-ba26-6c347915f16d",
    "_uuid": "a017f13dce80c88ce2f9fc0d63c374c93e827baa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9219, 28)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smd = md[md['id'].isin(links_small)]\n",
    "smd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "6c221b12-c5fd-4eb6-b61a-b7d3f395d2ab",
    "_uuid": "7ff7d25e2c175f2d71e02cf408ed113e10a148f1"
   },
   "outputs": [],
   "source": [
    "smd['cast'] = smd['cast'].apply(literal_eval)\n",
    "smd['crew'] = smd['crew'].apply(literal_eval)\n",
    "smd['keywords'] = smd['keywords'].apply(literal_eval)\n",
    "smd['cast_size'] = smd['cast'].apply(lambda x: len(x))\n",
    "smd['crew_size'] = smd['crew'].apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "0e19fe24-e206-4743-a96c-fb828707c74d",
    "_uuid": "4207baed79a12ab3314fc816076bbff032d3e186"
   },
   "outputs": [],
   "source": [
    "def get_director(x):\n",
    "    for i in x:\n",
    "        if i['job'] == 'Director':\n",
    "            return i['name']\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "d4468b32-a67e-4e31-bd56-1f4467884d1d",
    "_uuid": "64c84be00eb7eb7c0b9c4d9a1605e247c11ce2c6"
   },
   "outputs": [],
   "source": [
    "smd['director'] = smd['crew'].apply(get_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "cc3ff524-20c5-478c-b45e-9875c473e48b",
    "_uuid": "fd58e3dc015c81678ca1181a8de9c6e967ffdb22"
   },
   "outputs": [],
   "source": [
    "smd['cast'] = smd['cast'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n",
    "smd['cast'] = smd['cast'].apply(lambda x: x[:3] if len(x) >=3 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "2768dfe6-303a-4f88-81be-39b91c675202",
    "_uuid": "16c1509135d80d41051b8fb46ded3a6aad67e8f0"
   },
   "outputs": [],
   "source": [
    "smd['keywords'] = smd['keywords'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "6c5ffd70-ef6b-4fd0-be3d-b3c6a0d72df0",
    "_uuid": "01d86674d50b903fedc79750c4031ca88a739449"
   },
   "outputs": [],
   "source": [
    "smd['cast'] = smd['cast'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "5c1377ba-aad0-4a3f-8bcb-063c9c93f087",
    "_uuid": "7e16f8b131ff73d33463fd070774a24a5ce474df"
   },
   "outputs": [],
   "source": [
    "smd['director'] = smd['director'].astype('str').apply(lambda x: str.lower(x.replace(\" \", \"\")))\n",
    "smd['director'] = smd['director'].apply(lambda x: [x,x, x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5ea1aabb-a885-4dfe-a85e-0e31fd76558f",
    "_uuid": "feac137ab3987ade6ae3b041c6cbec3bf4364e66"
   },
   "source": [
    "#### Keywords\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "3dc8f545-4666-4cc0-8473-2870a69e7ff0",
    "_uuid": "5740d11531f7305f4edf13b0270dc014382a2b5c"
   },
   "outputs": [],
   "source": [
    "s = smd.apply(lambda x: pd.Series(x['keywords']),axis=1).stack().reset_index(level=1, drop=True)\n",
    "s.name = 'keyword'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "21e684ab-f6d3-4014-a05a-c28f995b4972",
    "_uuid": "1374a54bff3f7f0fdc872bfebff9f81d1dfcea69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "independent film        610\n",
       "woman director          550\n",
       "murder                  399\n",
       "duringcreditsstinger    327\n",
       "based on novel          318\n",
       "Name: keyword, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = s.value_counts()\n",
    "s[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "8cabd79b-dfab-46d8-922d-e204173d8103",
    "_uuid": "bf05fb571b1ee60335af20acb9cf5d7fee6254e8"
   },
   "outputs": [],
   "source": [
    "s = s[s > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "2d10427d-9339-46fa-b699-060b27d6d25f",
    "_uuid": "1d2a07e4de352df9d3e2230d36e691d166a1675c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dog'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemmer = SnowballStemmer('english')\n",
    "stemmer.stem('dogs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "5379c8d7-42a5-471a-84f8-bbd6cadf2e5c",
    "_uuid": "3bad77061d810e52b0d9fecfd26873c101167d00"
   },
   "outputs": [],
   "source": [
    "def filter_keywords(x):\n",
    "    words = []\n",
    "    for i in x:\n",
    "        if i in s:\n",
    "            words.append(i)\n",
    "    return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "959063e5-841d-408a-a10e-1f1437756b7c",
    "_uuid": "2a939eb8846871c606da0f654a1a9a978270e99f"
   },
   "outputs": [],
   "source": [
    "smd['keywords'] = smd['keywords'].apply(filter_keywords)\n",
    "smd['keywords'] = smd['keywords'].apply(lambda x: [stemmer.stem(i) for i in x])\n",
    "smd['keywords'] = smd['keywords'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "95c45262-2715-47b7-af65-1eb396c37d33",
    "_uuid": "6ff4da392e31cf628f91dd244cc4e76d63c40601"
   },
   "outputs": [],
   "source": [
    "smd['soup'] = smd['keywords'] + smd['cast'] + smd['director'] + smd['genres']\n",
    "smd['soup'] = smd['soup'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "7f9aa1bc-351b-47a1-b909-0d87efe93adc",
    "_uuid": "c758030ce02bb4beed606e41a3e0a51b659768f0"
   },
   "outputs": [],
   "source": [
    "count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n",
    "count_matrix = count.fit_transform(smd['soup'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "c462b8af-1eb8-499e-8e50-839811aebe9d",
    "_uuid": "defccf8a2c851672447b4f53ff1a8efddafccb33"
   },
   "outputs": [],
   "source": [
    "cosine_sim = cosine_similarity(count_matrix, count_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "d7ee34aa-da42-46bb-93e5-51e893286353",
    "_uuid": "ffa7edd804b3ea3065c6d428fbb613ae7cd203e3"
   },
   "outputs": [],
   "source": [
    "smd = smd.reset_index()\n",
    "titles = smd['title']\n",
    "indices = pd.Series(smd.index, index=smd['title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "e755e943-495b-46df-983b-2f8ad80fcd6a",
    "_uuid": "f6e2b6b232e992c783150a0ac5a7038c6de6db37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8031         The Dark Knight Rises\n",
       "6218                 Batman Begins\n",
       "6623                  The Prestige\n",
       "2085                     Following\n",
       "7648                     Inception\n",
       "4145                      Insomnia\n",
       "3381                       Memento\n",
       "8613                  Interstellar\n",
       "7659    Batman: Under the Red Hood\n",
       "1134                Batman Returns\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('The Dark Knight').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "ef4d3551-2c66-4732-9be1-10425962d5e9",
    "_uuid": "688b323c3b96224127465e8d48e67688ba4be202"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3319               Head Over Heels\n",
       "4763                 Freaky Friday\n",
       "1329              The House of Yes\n",
       "6277              Just Like Heaven\n",
       "7905         Mr. Popper's Penguins\n",
       "7332    Ghosts of Girlfriends Past\n",
       "6959     The Spiderwick Chronicles\n",
       "8883                      The DUFF\n",
       "6698         It's a Boy Girl Thing\n",
       "7377       I Love You, Beth Cooper\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Mean Girls').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "77c9d0d7-684c-41ab-b806-b38264b2cf3f",
    "_uuid": "536a4e8632d62d2b6a0c0d5e8b5731c39eb5529b"
   },
   "source": [
    "#### Popularity and Ratings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "1b5f0675-caa1-41bf-8a9a-ba89b0e07872",
    "_uuid": "0a7cc50433dcd13586dc1ee825b0880ad0c83869"
   },
   "outputs": [],
   "source": [
    "def improved_recommendations(title):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:26]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    \n",
    "    movies = smd.iloc[movie_indices][['title', 'vote_count', 'vote_average', 'year']]\n",
    "    vote_counts = movies[movies['vote_count'].notnull()]['vote_count'].astype('int')\n",
    "    vote_averages = movies[movies['vote_average'].notnull()]['vote_average'].astype('int')\n",
    "    C = vote_averages.mean()\n",
    "    m = vote_counts.quantile(0.60)\n",
    "    qualified = movies[(movies['vote_count'] >= m) & (movies['vote_count'].notnull()) & (movies['vote_average'].notnull())]\n",
    "    qualified['vote_count'] = qualified['vote_count'].astype('int')\n",
    "    qualified['vote_average'] = qualified['vote_average'].astype('int')\n",
    "    qualified['wr'] = qualified.apply(weighted_rating, axis=1)\n",
    "    qualified = qualified.sort_values('wr', ascending=False).head(10)\n",
    "    return qualified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "7f4ce0f9-ebc0-464d-a3d4-084f156d02b4",
    "_uuid": "c36a9662253e9a6bb1ae008e6cabd8dee7aaaa73"
   },
   "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>title</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>year</th>\n",
       "      <th>wr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7648</th>\n",
       "      <td>Inception</td>\n",
       "      <td>14075</td>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>7.917588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8613</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>11187</td>\n",
       "      <td>8</td>\n",
       "      <td>2014</td>\n",
       "      <td>7.897107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6623</th>\n",
       "      <td>The Prestige</td>\n",
       "      <td>4510</td>\n",
       "      <td>8</td>\n",
       "      <td>2006</td>\n",
       "      <td>7.758148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>Memento</td>\n",
       "      <td>4168</td>\n",
       "      <td>8</td>\n",
       "      <td>2000</td>\n",
       "      <td>7.740175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8031</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>9263</td>\n",
       "      <td>7</td>\n",
       "      <td>2012</td>\n",
       "      <td>6.921448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6218</th>\n",
       "      <td>Batman Begins</td>\n",
       "      <td>7511</td>\n",
       "      <td>7</td>\n",
       "      <td>2005</td>\n",
       "      <td>6.904127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>Batman Returns</td>\n",
       "      <td>1706</td>\n",
       "      <td>6</td>\n",
       "      <td>1992</td>\n",
       "      <td>5.846862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Batman Forever</td>\n",
       "      <td>1529</td>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
       "      <td>5.054144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9024</th>\n",
       "      <td>Batman v Superman: Dawn of Justice</td>\n",
       "      <td>7189</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>5.013943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>Batman &amp; Robin</td>\n",
       "      <td>1447</td>\n",
       "      <td>4</td>\n",
       "      <td>1997</td>\n",
       "      <td>4.287233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   title  vote_count  vote_average  year  \\\n",
       "7648                           Inception       14075             8  2010   \n",
       "8613                        Interstellar       11187             8  2014   \n",
       "6623                        The Prestige        4510             8  2006   \n",
       "3381                             Memento        4168             8  2000   \n",
       "8031               The Dark Knight Rises        9263             7  2012   \n",
       "6218                       Batman Begins        7511             7  2005   \n",
       "1134                      Batman Returns        1706             6  1992   \n",
       "132                       Batman Forever        1529             5  1995   \n",
       "9024  Batman v Superman: Dawn of Justice        7189             5  2016   \n",
       "1260                      Batman & Robin        1447             4  1997   \n",
       "\n",
       "            wr  \n",
       "7648  7.917588  \n",
       "8613  7.897107  \n",
       "6623  7.758148  \n",
       "3381  7.740175  \n",
       "8031  6.921448  \n",
       "6218  6.904127  \n",
       "1134  5.846862  \n",
       "132   5.054144  \n",
       "9024  5.013943  \n",
       "1260  4.287233  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improved_recommendations('The Dark Knight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "69a93363-d82c-47e3-94fc-d59a219a4f3a",
    "_uuid": "a04523fa7c17a9ae25d5fcade4c817d35e8b6f19"
   },
   "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>title</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>year</th>\n",
       "      <th>wr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>The Breakfast Club</td>\n",
       "      <td>2189</td>\n",
       "      <td>7</td>\n",
       "      <td>1985</td>\n",
       "      <td>6.709602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Dazed and Confused</td>\n",
       "      <td>588</td>\n",
       "      <td>7</td>\n",
       "      <td>1993</td>\n",
       "      <td>6.254682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8883</th>\n",
       "      <td>The DUFF</td>\n",
       "      <td>1372</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>5.818541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3712</th>\n",
       "      <td>The Princess Diaries</td>\n",
       "      <td>1063</td>\n",
       "      <td>6</td>\n",
       "      <td>2001</td>\n",
       "      <td>5.781086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4763</th>\n",
       "      <td>Freaky Friday</td>\n",
       "      <td>919</td>\n",
       "      <td>6</td>\n",
       "      <td>2003</td>\n",
       "      <td>5.757786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6277</th>\n",
       "      <td>Just Like Heaven</td>\n",
       "      <td>595</td>\n",
       "      <td>6</td>\n",
       "      <td>2005</td>\n",
       "      <td>5.681521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6959</th>\n",
       "      <td>The Spiderwick Chronicles</td>\n",
       "      <td>593</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>5.680901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7494</th>\n",
       "      <td>American Pie Presents: The Book of Love</td>\n",
       "      <td>454</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>5.119690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7332</th>\n",
       "      <td>Ghosts of Girlfriends Past</td>\n",
       "      <td>716</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>5.092422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7905</th>\n",
       "      <td>Mr. Popper's Penguins</td>\n",
       "      <td>775</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.087912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        title  vote_count  vote_average  year  \\\n",
       "1547                       The Breakfast Club        2189             7  1985   \n",
       "390                        Dazed and Confused         588             7  1993   \n",
       "8883                                 The DUFF        1372             6  2015   \n",
       "3712                     The Princess Diaries        1063             6  2001   \n",
       "4763                            Freaky Friday         919             6  2003   \n",
       "6277                         Just Like Heaven         595             6  2005   \n",
       "6959                The Spiderwick Chronicles         593             6  2008   \n",
       "7494  American Pie Presents: The Book of Love         454             5  2009   \n",
       "7332               Ghosts of Girlfriends Past         716             5  2009   \n",
       "7905                    Mr. Popper's Penguins         775             5  2011   \n",
       "\n",
       "            wr  \n",
       "1547  6.709602  \n",
       "390   6.254682  \n",
       "8883  5.818541  \n",
       "3712  5.781086  \n",
       "4763  5.757786  \n",
       "6277  5.681521  \n",
       "6959  5.680901  \n",
       "7494  5.119690  \n",
       "7332  5.092422  \n",
       "7905  5.087912  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "improved_recommendations('Mean Girls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "812fc4c7-b334-4f54-a05f-8fc0e7962a92",
    "_uuid": "ad1edcae8eecd5c725edbe18082bc4d8103ca800"
   },
   "source": [
    "## GUI with Tkinter\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *  \n",
    "import sys\n",
    "import tkinter.messagebox\n",
    "top = Tk()  \n",
    "\n",
    "def button_called():\n",
    "    if(Gname.get()==\"\"):\n",
    "        df1=improved_recommendations(Mname.get())\n",
    "        df2=df1['title']\n",
    "    else:\n",
    "        df1=build_chart(Gname.get()).head(15)\n",
    "        df2=df1['title']\n",
    "    messagebox.showinfo(\"Recommendations\",str(df2))\n",
    "\n",
    "top.geometry(\"400x250\")\n",
    "#top.configure(background='pink')\n",
    "top.title(\"Movie Recommendation\")\n",
    "  \n",
    "movie = Label(top, text = \"Enter movie name:\",font=(\"arial\",20,\"bold\"),fg='steelblue').place(x = 30,y = 20)  \n",
    "aur=Label(top,text=\"or\",font=(\"arial\",20,\"bold\"),fg='black').place(x=160,y=95) \n",
    "genre = Label(top, text = \"Enter Genre:\",font=(\"arial\",20,\"bold\"),fg='steelblue').place(x = 30, y = 130)  \n",
    "  \n",
    "sbmitbtn = Button(top, text = \"Recommend!\",width=30,height=1,activebackground = \"pink\", activeforeground = \"blue\", command = button_called).place(x = 100, y = 210)  \n",
    "Mname = StringVar()\n",
    "Gname = StringVar()\n",
    "e1 = Entry(top, textvariable=Mname).place(x = 40, y = 70)  \n",
    "\n",
    "e2 = Entry(top, textvariable=Gname).place(x = 40, y = 170)  \n",
    "\n",
    "top.mainloop()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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