Movie Recommender System .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4807ad10",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89fc2207",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('tmdb_5000_movies.csv')\n",
    "credits = pd.read_csv('tmdb_5000_credits.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75f4b2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3126e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "credits.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "318571ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies.merge(credits, on = 'title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c8955f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now I have to make content based mrs so I will search for cols that are required in this-\n",
    "## genres(horror)\n",
    "## id is imp \n",
    "## Keywords\n",
    "## title \n",
    "## overview\n",
    "## cast and crew "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8360962e",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies[['id','title','overview','genres','keywords','cast','crew']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d41a5b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51d76ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we will make another dataframe and add 3 cols in it - movie id, title and tags- (overview, genres, krywords, cast,crew)\n",
    "## In cast- we will take only top 3 and in crew only director name.\n",
    "## 1) We will check null data first \n",
    "## 2) We will check for duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7421817",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8c7a02c",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1f37ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cda2e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf1cf62d",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.iloc[0].genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "854bc1db",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we need to convert this dict into the name but the problem is that we first need to convert \n",
    "## list of int/string to list so we will import a function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9377be15",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3458b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(text):\n",
    "    L = []\n",
    "    for i in ast.literal_eval(text):\n",
    "        L.append(i['name']) \n",
    "    return L "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9776b93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we will apply this to the dataset and improve our generes section\n",
    "movies['genres'] = movies['genres'].apply(convert)\n",
    "movies.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9c4d23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['keywords'] = movies['keywords'].apply(convert)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52c8e7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert3(text):\n",
    "    L = []\n",
    "    counter=0\n",
    "    for i in ast.literal_eval(text):\n",
    "        if counter!=3:\n",
    "            L.append(i['name'])\n",
    "            counter+=1\n",
    "        else:\n",
    "            break\n",
    "        \n",
    "    return L "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd3e403f",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['cast'].apply(convert3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76096ac0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a680ae20",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head(1)\n",
    "### only crew left now in which we need director "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a379de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['crew'][0]\n",
    "## in this we need the job title to be director and its name in dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34305216",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_director(text):\n",
    "    L = []\n",
    "    for i in ast.literal_eval(text):\n",
    "        if i['job'] == 'Director':\n",
    "            L.append(i['name'])\n",
    "    return L "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18cae623",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['crew'] = movies['crew'].apply(fetch_director)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d689479e",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c63c308e",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['overview'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4235087",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6751a5e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e231333",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffcf55dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert3(text):\n",
    "    L = []\n",
    "    counter = 0\n",
    "    for i in ast.literal_eval(text):\n",
    "        if counter < 3:\n",
    "            L.append(i['name'])\n",
    "        counter+=1\n",
    "    return L "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f02bfb2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['cast'] = movies['cast'].apply(convert3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f77562",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7e77ebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now the issue is that while creating tags eg- Johny Depp are 2 diff words so we need to create 1 tage we will use lambda func\n",
    "def collapse(L):\n",
    "    L1 = []\n",
    "    for i in L:\n",
    "        L1.append(i.replace(\" \",\"\"))\n",
    "    return L1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2ae17a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['cast'] = movies['cast'].apply(collapse)\n",
    "movies['crew'] = movies['crew'].apply(collapse)\n",
    "movies['genres'] = movies['genres'].apply(collapse)\n",
    "movies['keywords'] = movies['keywords'].apply(collapse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23e212c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3184f8cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we will create one tags col in which we will conacatenate all these 5 cols \n",
    "movies['overview'] = movies['overview'].apply(lambda x:x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eee23d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "326d4c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head(5)## new col created tags under which overview, genres,etc all added "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2720ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we need to remove cols rest one \n",
    "new_df=movies[['id','title','tags']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581f38b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2df736c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'] = new_df['tags'].apply(lambda x: \" \".join(x))\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4d0413f",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a4732e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['tags']=new_df['tags'].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "116c3b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56cae3c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we have to start with text vectorization and before that if we understand our problem statement, \n",
    "## it would take a movie as an input and return the top 5 movies same as that. \n",
    "# 1 method is to compare the no. of similar words in both tags and calculate top 5 with max similar words\n",
    "## but rather than this we can do vectorization-convert string to vector\n",
    "## we will assign every vector a movie and if we select a movie, we will pick the five closest vectors \n",
    "## text->vectors(text vectorization) *bag of words, ifidf, word2vec\n",
    "## we will concatenate all tags suppose / tag 100 words total =500000\n",
    "## now out of these 500000 words i will select the top 5000 most repetitive words\n",
    "## we will create a 2D matrix every row is a vector and m1- how many times the most occured 5000th word (5000,5000)\n",
    "## check best results kisme mil rhe hai 2000 5000 10,000 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22de3c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=5000,stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b00f0a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = cv.fit_transform(new_df['tags']).toarray()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cef45641",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector.shape ## movies- 4806 and 5000 top words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac701721",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c93f1ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ba0f90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(cv.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62baf2a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we have words where action actions activity activities etc repetitive words so we will use nltk lib and \n",
    "## import stem which converts similar words to 1 word "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f14d82e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5d949d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps=PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "598a2953",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stem(text):\n",
    "    y=[]\n",
    "    for i in text.split():\n",
    "        y.append(ps.stem(i))\n",
    "    string=\" \".join(y)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10b707a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##new_df['tags']=new_df['tags'].apply(stem) ## 1:16:51 this was not working "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "92a04544",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we will have to calculate the distance of every movie from the particular movie selected\n",
    "## more the distance, less related the movies are(as the movies now are vectors)\n",
    "## we will not calculate the euclidean distance, we will calculate the cosine distance \n",
    "## as the dimensionality increases, the measure of euclidean fails \n",
    "## we will use cosine similarity if 0- less similar if 1 then more similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3ac1cff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c364780c",
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = cosine_similarity(vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "20fa51c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4806, 4806)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity.shape ## it will be 4806x4806 that is it will have these dimensions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dc1205f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.08964215, 0.06071767, ..., 0.02519763, 0.0277885 ,\n",
       "       0.        ])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity[0] ## here we have the similarity status of the first movie with every movie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "62e794ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we will create a recommend function which will give top 5 similar movies. \n",
    "## enumerate list func- it is used because if we sort the array without that, then we will losse our index values\n",
    "def recommend(movie):\n",
    "    index = new_df[new_df['title'] == movie].index[0]\n",
    "    distances = sorted(list(enumerate(similarity[index])),reverse=True,key = lambda x: x[1])\n",
    "    for i in distances[1:6]:\n",
    "        print(new_df.iloc[i[0]].title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2ea1f127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "distances = sorted(list(enumerate(similarity[0])),reverse=True,key = lambda x: x[1])\n",
    "print(distances) ## in this way it will not be of any trouble\n",
    "## the lambda function tells that we have to sort from second col "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4051a35a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Dark Knight\n",
      "The Dark Knight Rises\n",
      "Batman\n",
      "Batman & Robin\n",
      "Batman\n"
     ]
    }
   ],
   "source": [
    "recommend('Batman Begins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6056518a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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