Merge pull request #388 from Kirti-Pant/patch-1

Create MovieRecommendationSystemMLProject.ipynb

Author: kaal-coder

MovieRecommendationSystemMLProject.ipynb

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+import pandas as pd
+import numpy as np
+import difflib
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+data=pd.read_csv('/content/movies.csv')
+data.head(5)
+data.tail(5)
+data.info()
+data.shape
+selected_features=['genres','keywords','tagline','cast','director']
+data.isnull().sum()
+selected_features
+data.isnull().sum()
+for feature in selected_features:
+  data[feature]=data[feature].fillna('')
+data.isnull().sum()
+combined_features=data['genres']+' '+ data['keywords'] +' ' + data['tagline']+' '+ data['cast']+' '+ data['director']
+combined_features
+vectorizer=TfidfVectorizer()
+feature_vectors=vectorizer.fit_transform(combined_features)
+feature_vectors
+similarity=cosine_similarity(feature_vectors)
+print(similarity)
+similarity.shape 
+movie_name=input("Enter your favourite movie name: ")
+list_of_titles=data['title'].tolist()
+find_close_max=difflib.get_close_matches(movie_name, list_of_titles)
+close_match=find_close_max[0]
+index_of_movie=data[data.title== close_match]['index'].values[0]
+similarity_score=list(enumerate(similarity[index_of_movie]))
+sorted_similar_movies=sorted(similarity_score, key=lambda x:x[1],reverse=True )
+print('Movies suggested for you : \n')
+i=1
+for movie in sorted_similar_movies:
+  index=movie[0]
+  title_from_index=data[data.index==index]['title'].values[0]
+  if i<=30:
+    print(i,' ',title_from_index)
+    i+=1