├── README.md
└── Movie


/README.md:
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1 | # Movie-Recommendation-System
2 | Develop a site that analyzes user preferences and recommends movies based on their taste
3 | 


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/Movie:
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 1 | import pandas as pd
 2 | from sklearn.feature_extraction.text import CountVectorizer
 3 | from sklearn.metrics.pairwise import cosine_similarity
 4 | 
 5 | # Sample dataset
 6 | data = {'title': ['Movie1', 'Movie2', 'Movie3'], 
 7 |         'genre': ['Action Comedy', 'Drama', 'Action Drama']}
 8 | df = pd.DataFrame(data)
 9 | 
10 | def recommend_movies(title):
11 |     count = CountVectorizer()
12 |     matrix = count.fit_transform(df['genre'])
13 |     similarity = cosine_similarity(matrix)
14 |     indices = pd.Series(df.index, index=df['title'])
15 |     idx = indices[title]
16 |     sim_scores = list(enumerate(similarity[idx]))
17 |     sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
18 |     sim_scores = sim_scores[1:]
19 |     return [df['title'][i[0]] for i in sim_scores]
20 | 
21 | print(recommend_movies('Movie1'))
22 | 


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