app.py

movie_recommendation_system.py

@@ -0,0 +1,105 @@
+# -*- 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