Movie_recommender_system.ipynb

import numpy as np
import pandas as pd

from google.colab import drive
drive.mount('/content/drive')

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

movies = pd.read_csv('/content/drive/MyDrive/tmdb_5000_movies.csv')
credits = pd.read_csv('/content/drive/MyDrive/tmdb_5000_credits.csv')

movies.head(2)

movies.shape


credits.head()

credits.shape

movies = movies.merge(credits,on='title')

movies.head()

movies = movies[['movie_id','title','overview','genres','keywords','cast','crew']]

movies.head()

import ast

def convert(text):
    L = []
    for i in ast.literal_eval(text):
        L.append(i['name'])
    return L

movies.dropna(inplace=True)

movies['genres'] = movies['genres'].apply(convert)
movies.head()

movies['keywords'] = movies['keywords'].apply(convert)
movies.head()

import ast
ast.literal_eval('[{"id": 28, "name": "Action"}, {"id": 12, "name": "Adventure"}, {"id": 14, "name": "Fantasy"}, {"id": 878, "name": "Science Fiction"}]')

import ast
ast.literal_eval('[{"id": 28, "name": "Action"}, {"id": 12, "name": "Adventure"}, {"id": 14, "name": "Fantasy"}, {"id": 878, "name": "Science Fiction"}]')
def convert3(text):
    L = []
    counter = 0
    for i in ast.literal_eval(text):
        if counter < 3:
            L.append(i['name'])
        counter+=1
    return L

movies['cast'] = movies['cast'].apply(convert)
movies.head()

movies['cast'] = movies['cast'].apply(lambda x:x[0:3])

def fetch_director(text):
    L = []
    for i in ast.literal_eval(text):
        if i['job'] == 'Director':
            L.append(i['name'])
    return L


movies['crew'] = movies['crew'].apply(fetch_director)

movies.sample(5)

def collapse(L):
    L1 = []
    for i in L:
        L1.append(i.replace(" ",""))
    return L1

movies['cast'] = movies['cast'].apply(collapse)
movies['crew'] = movies['crew'].apply(collapse)
movies['genres'] = movies['genres'].apply(collapse)
movies['keywords'] = movies['keywords'].apply(collapse)

movies.head()

movies['overview'] = movies['overview'].apply(lambda x:x.split())

movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']

new = movies.drop(columns=['overview','genres','keywords','cast','crew'])

new['tags'] = new['tags'].apply(lambda x: " ".join(x))
new.head()

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features=5000,stop_words='english')

vector = cv.fit_transform(new['tags']).toarray()

vector.shape

from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity(vector)

similarity

new[new['title'] == 'The Lego Movie'].index[0]

def recommend(movie):
    index = new[new['title'] == movie].index[0]
    distances = sorted(list(enumerate(similarity[index])),reverse=True,key = lambda x: x[1])
    for i in distances[1:6]:
        print(new.iloc[i[0]].title)


recommend('Gandhi')

import pickle

with open('/content/drive/MyDrive/movie_list.pkl', 'wb') as f:
    pickle.dump(new, f)

with open('/content/drive/MyDrive/similarity.pkl', 'wb') as f:
    pickle.dump(similarity, f)