Learning Machine Learning: Building a Movie Recommendation System

🎬 Building a movie recommendation system

Want to learn machine learning with a real project? Recommendation systems, like Netflix’s or Amazon’s, are a perfect opportunity.

📌 What will you learn?

🔁 Collaborative filtering: recommends based on tastes of similar users.
📄 Content-based filtering: suggests based on attributes of movies you’ve already seen.

To start, use the famous MovieLens dataset with ratings and metadata.

🧠 Code example (Python)
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import pandas as pd
from surprise import Dataset, Reader, SVD
from surprise.model_selection import train_test_split
from surprise.accuracy import rmse

# load ratings
df = pd.read_csv('ratings.csv')  # part of MovieLens
reader = Reader(rating_scale=(0.5, 5.0))
data = Dataset.load_from_df(df[['userId','movieId','rating']], reader)

trainset, testset = train_test_split(data, test_size=0.2)
algo = SVD(n_factors=50, random_state=42)
algo.fit(trainset)

predictions = algo.test(testset)
rmse(predictions)

✨ Brief explanation
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Imagine you’re Netflix: millions of users and movies.

With collaborative filtering you look at what other people with similar tastes watched and use their ratings to guess what you might like.
The SVD algorithm decomposes the ratings matrix into latent factors (hidden tastes).
It’s trained on part of the data and measured on how well it predicts on another (RMSE).

It’s a simple way to see machine learning in action: data → model → prediction → evaluation. And the best part: you can start today with MovieLens!