Recommendation engines are a powerful tool used in various domains like e-commerce, entertainment, and social media. This tutorial guides you through building a basic movie recommendation engine using Python and the Pandas library.
1. Setting Up:
- Ensure you have Python installed. If not, download it from https://www.python.org/
- Install the Pandas library using pip:
pip install pandas
2. Data Preparation:
- We’ll use a simplified movie rating dataset. You can create your own CSV file or use an existing one from sources like https://grouplens.org/datasets/movielens/.
- The CSV should have columns like ‘user_id’, ‘movie_id’, and ‘rating’.
- Load the data into a Pandas DataFrame:
import pandas as pd data = pd.read_csv('movie_ratings.csv')
3. Collaborative Filtering:
- Collaborative filtering is a popular approach for recommendations. It leverages the idea that users who agreed in the past tend to agree in the future.
- We’ll use a simple correlation-based method. Calculate the correlation between movie ratings:
movie_correlation = data.pivot_table(index='user_id', columns='movie_id', values='rating').corr()
4. Making Recommendations:
- Let’s say a user has watched and rated movie with ID ‘1’. We can recommend movies based on their correlation with this movie:
similar_movies = movie_correlation['1'].sort_values(ascending=False).head(10) # Get top 10 correlated movies recommended_movies = similar_movies.index.tolist() # Extract movie IDs
Pro-Tips:
- Experiment with different similarity metrics like cosine similarity or Euclidean distance.
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Explore more advanced collaborative filtering techniques like matrix factorization or user-based methods.
Artificial Intelligence, Machine Learning, Data Science, Python, Recommendation Engine, Pandas