Build a Personalized Recommendation System Using Collaborative Filtering | Data Science Project

This project focuses on building a personalized recommendation system, similar to what is used by: Netflix (movies) Amazon (products) YouTube (videos) Spotify (music) The system recommends items based on user behavior and preferences. Recommendation systems drive: User engagement Revenue Retention This is one of the most important and frequently asked data science projects. 🧰 TOOLS & TECHNOLOGIES USED Programming & Analytics Python 3.10+ Pandas, NumPy Machine Learning Scikit-learn Surprise Library (for recommender systems) Visualization Matplotlib / Seaborn Utilities Git & GitHub 📁 PROJECT FOLDER STRUCTURE recommendation_system/ │ ├── data/ │ └── ratings.csv │ ├── preprocessing/ │ └── build_matrix.py │ ├── models/ │ └── collaborative_filtering.py │ ├── evaluation/ │ └── metrics.py │ ├── recommendations/ │ └── recommend.py │ ├── requirements.txt └── README.md 📂 DATA REQUIRED Use datasets like: Movie ratings Product reviews User interactions Typical format: user_id item_id rating timestamp Example: 1,101,5 1,102,4 2,101,3 🧠 STEP-BY-STEP IMPLEMENTATION 🔹 STEP 1: Load Data import pandas as pd df = pd.read_csv("data/ratings.csv") 🔹 STEP 2: Create User-Item Matrix user_item_matrix = df.pivot_table( index='user_id', columns='item_id', values='rating' ).fillna(0) This matrix represents user preferences. 🔹 STEP 3: Compute Similarity from sklearn.metrics.pairwise import cosine_similarity user_similarity = cosine_similarity(user_item_matrix) Find users with similar tastes. 🔹 STEP 4: Predict Ratings predicted_ratings = user_similarity.dot(user_item_matrix) This estimates missing ratings. 🔹 STEP 5: Generate Recommendations def recommend(user_id, top_n=5): user_row = predicted_ratings[user_id] return user_row.argsort()[-top_n:] Recommend top items not yet seen. 🔹 STEP 6: Matrix Factorization (Advanced) from surprise import SVD model = SVD() model.fit(trainset) More accurate predictions using latent f