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+**Movie Recommendation System**
+
+**Library**
+
+import pandas as pd
+
+import numpy as np
+
+**Dataset**
+
+df = pd.read_csv('https://raw.githubusercontent.com/YBIFoundation/Dataset/main/Movies%20Recommendation.csv')
+
+df.head()
+
+df.info()
+
+df.shape
+
+df.columns
+
+**Get Feature**
+
+df_features = df[['Movie_Genre','Movie_Keywords','Movie_Tagline', 'Movie_Cast', 'Movie_Director']].fillna('')
+
+df_features.shape
+
+df_features
+
+X = df_features['Movie_Genre'] + '' +df_features['Movie_Tagline'] + '' +df_features['Movie_Cast'] + '' +df_features['Movie_Director']
+
+X
+
+X.shape
+
+**Feature Text Conversion to Tokens**
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+tfidf = TfidfVectorizer()
+
+X = tfidf.fit_transform(X)
+
+X.shape
+
+print(X)
+
+**Cosine Similarity**
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+Similarity_Score = cosine_similarity(X)
+
+Similarity_Score
+
+Similarity_Score.shape
+
+**Movie Name as Input from User and Validate for Closest Spelling**
+
+Favourite_Movie_Name = input(' Enter your favourite movie name : ')
+
+All_Movies_Title_List = df['Movie_Title'].tolist()
+
+import difflib
+
+Movie_Recommendation = difflib.get_close_matches(Favourite_Movie_Name, All_Movies_Title_List)
+print(Movie_Recommendation)
+
+Close_Match = Movie_Recommendation[0]
+print(Close_Match)
+
+Index_of_Close_Match_Movie = df[df.Movie_Title == Close_Match]['Movie_ID'].values[0]
+print(Index_of_Close_Match_Movie)
+
+#getting a list of similar movies
+Recommendation_score = list(enumerate(Similarity_Score[Index_of_Close_Match_Movie]))
+print(Recommendation_score)
+
+len(Recommendation_score)
+
+Sorted_Similar_Movies = sorted(Recommendation_score, key= lambda x:x[1], reverse= True)
+print(Sorted_Similar_Movies)
+
+print('Top 30 Movies Suggested for You : \n')
+
+i=1
+
+for movie in Sorted_Similar_Movies:
+  index = movie[0]
+  title_from_index = df[df.index==index]['Movie_Title'].values[0]
+  if (i<31):
+    print(i, '.',title_from_index)
+    i+=1