Content based recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc26085c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "from sklearn.metrics import jaccard_score\n",
    "\n",
    "import pickle\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68147d8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Unnamed: 0          1000 non-null   int64  \n",
      " 1   Title               1000 non-null   object \n",
      " 2   Year of Release     999 non-null    object \n",
      " 3   Watch Time          952 non-null    object \n",
      " 4   Genre               1000 non-null   object \n",
      " 5   Movie Rating        943 non-null    float64\n",
      " 6   Metascore of movie  909 non-null    float64\n",
      " 7   Director            1000 non-null   object \n",
      " 8   Cast                1000 non-null   object \n",
      " 9   Votes               943 non-null    object \n",
      " 10  Description         1000 non-null   object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 86.1+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>movie_id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Year of Release</th>\n",
       "      <th>Watch Time</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Movie Rating</th>\n",
       "      <th>Metascore of movie</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>No Time to Die</td>\n",
       "      <td>2021</td>\n",
       "      <td>163 min</td>\n",
       "      <td>\\nAction, Adventure, Thriller</td>\n",
       "      <td>7.7</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Cary Joji Fukunaga</td>\n",
       "      <td>Daniel Craig,Ana de Armas,Rami Malek,Léa Seydoux</td>\n",
       "      <td>35,153</td>\n",
       "      <td>James Bond has left active service. His peace ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dune</td>\n",
       "      <td>2021</td>\n",
       "      <td>155 min</td>\n",
       "      <td>\\nAction, Adventure, Drama</td>\n",
       "      <td>8.4</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Denis Villeneuve</td>\n",
       "      <td>Timothée Chalamet,Rebecca Ferguson,Zendaya,Osc...</td>\n",
       "      <td>63,210</td>\n",
       "      <td>Feature adaptation of Frank Herbert's science ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Free Guy</td>\n",
       "      <td>2021</td>\n",
       "      <td>115 min</td>\n",
       "      <td>\\nAction, Adventure, Comedy</td>\n",
       "      <td>7.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>Shawn Levy</td>\n",
       "      <td>Ryan Reynolds,Jodie Comer,Taika Waititi,Lil Re...</td>\n",
       "      <td>117,171</td>\n",
       "      <td>A bank teller discovers that he's actually an ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>The Many Saints of Newark</td>\n",
       "      <td>2021</td>\n",
       "      <td>120 min</td>\n",
       "      <td>\\nCrime, Drama</td>\n",
       "      <td>6.6</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Alan Taylor</td>\n",
       "      <td>Alessandro Nivola,Leslie Odom Jr.,Jon Bernthal...</td>\n",
       "      <td>15,173</td>\n",
       "      <td>A look at the formative years of New Jersey ga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Venom: Let There Be Carnage</td>\n",
       "      <td>2021</td>\n",
       "      <td>97 min</td>\n",
       "      <td>\\nAction, Adventure, Sci-Fi</td>\n",
       "      <td>6.6</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Andy Serkis</td>\n",
       "      <td>Tom Hardy,Woody Harrelson,Michelle Williams,Na...</td>\n",
       "      <td>10,107</td>\n",
       "      <td>Eddie Brock attempts to reignite his career by...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                        Title Year of Release Watch Time  \\\n",
       "0         0               No Time to Die            2021    163 min   \n",
       "1         1                         Dune            2021    155 min   \n",
       "2         2                     Free Guy            2021    115 min   \n",
       "3         3    The Many Saints of Newark            2021    120 min   \n",
       "4         4  Venom: Let There Be Carnage            2021     97 min   \n",
       "\n",
       "                                       Genre  Movie Rating  \\\n",
       "0  \\nAction, Adventure, Thriller                       7.7   \n",
       "1     \\nAction, Adventure, Drama                       8.4   \n",
       "2    \\nAction, Adventure, Comedy                       7.3   \n",
       "3                 \\nCrime, Drama                       6.6   \n",
       "4    \\nAction, Adventure, Sci-Fi                       6.6   \n",
       "\n",
       "   Metascore of movie            Director  \\\n",
       "0                70.0  Cary Joji Fukunaga   \n",
       "1                76.0    Denis Villeneuve   \n",
       "2                62.0          Shawn Levy   \n",
       "3                61.0         Alan Taylor   \n",
       "4                47.0         Andy Serkis   \n",
       "\n",
       "                                                Cast    Votes  \\\n",
       "0   Daniel Craig,Ana de Armas,Rami Malek,Léa Seydoux   35,153   \n",
       "1  Timothée Chalamet,Rebecca Ferguson,Zendaya,Osc...   63,210   \n",
       "2  Ryan Reynolds,Jodie Comer,Taika Waititi,Lil Re...  117,171   \n",
       "3  Alessandro Nivola,Leslie Odom Jr.,Jon Bernthal...   15,173   \n",
       "4  Tom Hardy,Woody Harrelson,Michelle Williams,Na...   10,107   \n",
       "\n",
       "                                         Description  \n",
       "0  James Bond has left active service. His peace ...  \n",
       "1  Feature adaptation of Frank Herbert's science ...  \n",
       "2  A bank teller discovers that he's actually an ...  \n",
       "3  A look at the formative years of New Jersey ga...  \n",
       "4  Eddie Brock attempts to reignite his career by...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel(\"movie_data.xlsx\")\n",
    "data.info()\n",
    "data.rename(columns={'Unnamed: 0': 'movie_id'}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c414b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=['Cast','Director','Genre','Title','Description']\n",
    "columns2=['Cast','Director','Genre','Title', 'Movie Rating','Description']\n",
    "columns3=['Cast','Director','Genre','Title','Metascore of movie','Description']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0792338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[columns].isnull().values.any()#no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a1cb2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_important_features(data):\n",
    "    important_features=[]\n",
    "    for i in range (0,data.shape[0]):\n",
    "        important_features.append(data['Title'][i]+' '+data['Director'][i]+' '+data['Genre'][i]+' '+data['Description'][i])\n",
    "    return important_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7cb413a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 6709)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a column to hold the combined strings\n",
    "data['important_features']=get_important_features(data)\n",
    "\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "feature_matrix = tfidf.fit_transform(data['important_features'])\n",
    "feature_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba493274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Movie Similarity Checker\n",
    "cosine_sim = linear_kernel(feature_matrix, feature_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94cc8567",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = pd.Series(data.index, index=data['Title']).drop_duplicates()\n",
    "\n",
    "def get_recommendations(title, cosine_sim=cosine_sim):\n",
    "    idx = indices[title]\n",
    "    # Get the pairwsie similarity scores of all movies with that movie\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    \n",
    "    # Sort the movies based on the similarity score\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:6]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    \n",
    "    # Return the top 5 most similar movies\n",
    "    movies=data['Title'].iloc[movie_indices]\n",
    "    id=data['movie_id'].iloc[movie_indices]\n",
    "    dict={\"Movies\":movies,\"id\":id}\n",
    "    final_df=pd.DataFrame(dict)\n",
    "    final_df.reset_index(drop=True,inplace=True)\n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de47fdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Movies</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Venom</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Silence of the Lambs</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Escape from New York</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Birdman or (The Unexpected Virtue of Ignorance)</td>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Let Him Go</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Movies   id\n",
       "0                                            Venom   20\n",
       "1                         The Silence of the Lambs  196\n",
       "2                             Escape from New York  919\n",
       "3  Birdman or (The Unexpected Virtue of Ignorance)  892\n",
       "4                                       Let Him Go  450"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Movie Recommendation - Cosine\n",
    "get_recommendations('Venom: Let There Be Carnage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7204ffd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Movies</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spider-Man: No Way Home</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spider-Man: Homecoming</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spider-Man: Into the Spider-Verse</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Amazing Spider-Man 2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spider-Man</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Movies   id\n",
       "0            Spider-Man: No Way Home   36\n",
       "1             Spider-Man: Homecoming  227\n",
       "2  Spider-Man: Into the Spider-Verse  233\n",
       "3           The Amazing Spider-Man 2  500\n",
       "4                         Spider-Man  138"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Movie Recommendation - Cosine\n",
    "get_recommendations('Spider-Man: Far from Home')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4afd4301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movies</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Sudden Move</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Descent</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kill Bill: Vol. 1</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Hateful Eight</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Movies   id\n",
       "0     No Sudden Move  346\n",
       "1        The Descent  783\n",
       "2  Kill Bill: Vol. 1  440\n",
       "3  The Hateful Eight  433\n",
       "4       Pulp Fiction  112"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Movie Recommendation - Cosine\n",
    "get_recommendations('Reservoir Dogs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5085721",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   movie_id            1000 non-null   int64  \n",
      " 1   Title               1000 non-null   object \n",
      " 2   Year of Release     999 non-null    object \n",
      " 3   Watch Time          952 non-null    object \n",
      " 4   Genre               1000 non-null   object \n",
      " 5   Movie Rating        943 non-null    float64\n",
      " 6   Metascore of movie  909 non-null    float64\n",
      " 7   Director            1000 non-null   object \n",
      " 8   Cast                1000 non-null   object \n",
      " 9   Votes               943 non-null    object \n",
      " 10  Description         1000 non-null   object \n",
      " 11  important_features  1000 non-null   object \n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 93.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n",
    "new = data.drop(columns=['Year of Release','Watch Time','Genre','Movie Rating','Metascore of movie','Director','Cast','Votes','Description'])\n",
    "\n",
    "pickle.dump(new,open('movie_list.pkl','wb'))\n",
    "pickle.dump(cosine_sim,open('similarity.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a6bff05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Building wheels for collected packages: blinker\n",
      "  Building wheel for blinker (setup.py): started\n",
      "  Building wheel for blinker (setup.py): finished with status 'done'\n",
      "  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13451 sha256=f5f9977b99a45aaac0297c5b3b614a71c57efa7ed789c7b424c9399d1de0080e\n",
      "  Stored in directory: c:\\users\\tottun\\appdata\\local\\pip\\cache\\wheels\\b7\\a5\\68\\fe632054a5eadd531c7a49d740c50eb6adfbeca822b4eab8d4\n",
      "Successfully built blinker\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing collected packages: smmap, tzdata, gitdb, backports.zoneinfo, validators, tzlocal, pydeck, pyarrow, protobuf, gitpython, cachetools, blinker, base58, astor, altair, streamlit\n",
      "Successfully installed altair-4.1.0 astor-0.8.1 backports.zoneinfo-0.2.1 base58-2.1.0 blinker-1.4 cachetools-4.2.4 gitdb-4.0.7 gitpython-3.1.24 protobuf-3.18.0 pyarrow-5.0.0 pydeck-0.7.0 smmap-4.0.0 streamlit-0.89.0 tzdata-2021.2.post0 tzlocal-3.0 validators-0.18.2\n"
     ]
    }
   ],
   "source": [
    "!pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a313a566",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}