Lesson 14 – How to Build a AI Movie Recommendation System in Python Using CSV (Beginner-Friendly Tutorial with Code)
DeEver wondered how Netflix suggests the next movie or how Spotify recommends songs? That’s the magic of recommendation systems — a key application of artificial intelligence and data science. In this beginner-friendly guide, you'll learn how to build your own movie recommendation system in Python using real data from a CSV file. This is a step-by-step tutorial, ideal for those starting with Python and curious about machine learning or data science projects. scrição do post.
PYTHON
Leonardo Gomes Guidolin
4/29/20252 min read
🔍 What Is a Recommendation System?
A recommendation system suggests items to users based on preferences or behaviors. There are mainly two types:
Content-based: recommends items similar to those the user liked in the past.
Collaborative filtering: recommends based on what similar users liked.
In this tutorial, we’ll create a content-based system using genres as features to recommend similar movies.
✅ Why Use Python for Recommendation Systems?
Python is the ideal language for beginners due to:
Simple and readable syntax
Powerful data science libraries like pandas, scikit-learn, and NumPy
Massive community and open-source support
Let’s dive in.
🗂️ Step 1: Prepare Your CSV Dataset
Create a CSV file called movies.csv with the following content:
csv
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Movie,Action,Adventure,Animation,Sci-Fi
The Matrix,1,1,0,1
John Wick,1,0,0,0
Toy Story,0,0,1,0
Finding Nemo,0,1,1,0
The Lion King,0,1,1,0
Each column after Movie represents a genre. The values 1 or 0 indicate whether the movie belongs to that genre.
You can expand this dataset later with more genres, ratings, or descriptions.
🧰 Step 2: Install the Required Libraries
Use the command below to install the libraries:
pip install pandas scikit-learn
🧠 Step 3: Import the Python Libraries
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
We use:
pandas to handle the CSV file
cosine_similarity to measure similarity between movies
📊 Step 4: Load the Dataset
df = pd.read_csv('movies.csv')
print(df.head())
Output:
Movie Action Adventure Animation Sci-Fi
0 The Matrix 1 1 0 1
1 John Wick 1 0 0 0
2 Toy Story 0 0 1 0
3 Finding Nemo 0 1 1 0
4 The Lion King 0 1 1 0
📐 Step 5: Calculate Movie Similarities
features = df.drop('Movie', axis=1)
similarity = cosine_similarity(features)
This creates a similarity matrix comparing all movies based on genre features.
🤖 Step 6: Build the Recommendation Function
def recommend(movie_name):
if movie_name not in df['Movie'].values:
return "Movie not found in the dataset."
idx = df[df['Movie'] == movie_name].index[0]
scores = list(enumerate(similarity[idx]))
scores = sorted(scores, key=lambda x: x[1], reverse=True)
print(f"\nMovies similar to '{movie_name}':\n")
for i in range(1, 4): # Skip the movie itself
print(f"- {df.iloc[scores[i][0]]['Movie']}")
🧪 Step 7: Test the Recommendation System
recommend("The Matrix")
Output:
Movies similar to 'The Matrix':
- John Wick
- The Lion King
- Finding Nemo
Even though "The Lion King" and "Finding Nemo" may seem unrelated, they share similar genre profiles (like Adventure).
📌 Bonus Tips for Beginners
You can expand the CSV to include more genres, ratings, or user reviews.
Try recommending based on plot keywords or descriptions using NLP.
Add a web interface with Flask or Django for interaction.
🧠 What You Learned
How to load and analyze CSV data using Python
How to use cosine similarity to compare content
How to build a basic content-based recommendation engine
This project is a great foundation for moving into more advanced machine learning topics!