Movie Recommendation Engine
Build a content-based movie recommender that suggests similar titles based on genre, cast, and description text using TF-IDF and cosine similarity. Learners practice text vectorization, similarity search, and packaging a small ML script into a reusable module.
- Estimate
- ~8.5h
- Steps
- 5
- Completed by
- 0
- Proposed by
- codeseed.app
pandas · scikit-learn
Project roadmap
- 01
Load and preprocess the movie dataset
~2hLoad a public movies metadata CSV, clean text fields, and combine genre, cast, and description into a single feature string.
- 02
Vectorize text with TF-IDF
~1.5hUse scikit-learn's TfidfVectorizer to transform the combined text features into a sparse matrix.
- 03
Compute similarity and recommendations
~2hCalculate cosine similarity between movies and write a function returning the top-N most similar titles for a given movie.
- 04
Build a simple query interface
~1.5hCreate a command-line interface where users type a movie title and receive ranked recommendations.
- 05
Evaluate and tune
~1.5hExperiment with weighting genre vs. description text and document how recommendations change.
Resources
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Tech stack