app.py

# -*- coding: utf-8 -*-
"""movie recommendation system.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1iy9ZFUvOu-sz4vP4--9-dr-T-8LMZNOP
"""

import numpy as np
import pandas as pd
import difflib
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import  cosine_similarity

movies_df = pd.read_csv('/content/movies.csv')

movies_df.head()

movies_df.shape

selected_features = ['genres', 'keywords', 'tagline', 'cast','director']

print(selected_features)

for feature in selected_features:
  movies_df[feature]=movies_df[feature].fillna('')

combined_features = movies_df['genres']+' '+movies_df['keywords']+' '+movies_df['tagline']+' '+movies_df['cast']+' '+movies_df['director']

print(combined_features)

vectorizer = TfidfVectorizer()

feature_vectors = vectorizer.fit_transform(combined_features)

print(feature_vectors)

similarity = cosine_similarity(feature_vectors)

print(similarity)

print(similarity.shape)

movie_name = input(' Enter name of the movie: ')

list_of_all_titles = movies_df['title'].tolist()
new_var = print(list_of_all_titles)

find_close_match = difflib.get_close_matches(movie_name, list_of_all_titles)
print(find_close_match)

close_match = find_close_match[0]
print(close_match)

index_of_the_movie = movies_df[movies_df.title == close_match]['index'].values[0]
print(index_of_the_movie)

similarity_score = list(enumerate(similarity[index_of_the_movie]))
print(similarity_score)

len(similarity_score)

sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True)
print(sorted_similar_movies)

print('suggesting movies for you : \n')

i = 1

for movie in sorted_similar_movies:
  index = movie[0]
  title_from_index = movies_df[movies_df.index==index]['title'].values[0]
  if (i<30):
    print(i, '.',title_from_index)
    i+=1

"""movie recommendation system

"""

movie_name = input(' which is your favourite movie : ')

list_of_all_titles = movies_df['title'].tolist()

find_close_match = difflib.get_close_matches(movie_name, list_of_all_titles)

close_match = find_close_match[0]

index_of_the_movie = movies_df[movies_df.title == close_match]['index'].values[0]

similarity_score = list(enumerate(similarity[index_of_the_movie]))

sorted_similar_movies = sorted(similarity_score, key = lambda x:x[1], reverse = True)

print('suggesting movies for you : \n')

i = 1

for movie in sorted_similar_movies:
  index = movie[0]
  title_from_index = movies_df[movies_df.index==index]['title'].values[0]
  if (i<30):
    print(i, '.',title_from_index)
    i+=1