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How do platforms like Amazon recommend the perfect products? In this machine learning project, we build a search and recommendation system for Amazon products, leveraging natural language processing (NLP) and collaborative filtering to deliver personalized shopping experiences. 📦 Whether you're a data scientist, ML engineer, or e-commerce entrepreneur, this project will guide you through the end-to-end development of a recommendation system, including data preprocessing, feature engineering, and model tuning. 📊 What You’ll Learn in This Machine Learning Project: ✅ Understanding Recommendation System Types – Content-based, Collaborative, and Hybrid ✅ Data Collection & Cleaning – Preparing real-world e-commerce data ✅ Building Product Search Algorithms – NLP for semantic search ✅ Collaborative Filtering – Personalized product recommendations ✅ Using Word2Vec / TF-IDF for text-based recommendations ✅ Evaluating Model Performance – Precision, recall, and F1 score 🎯 Who Should Watch This? ✔️ Data Scientists & ML Engineers building real-world projects ✔️ E-commerce developers & startup founders ✔️ AI enthusiasts looking to master recommendation systems ✔️ Students & beginners building ML portfolios 🚀 Make your recommendations smart, personalized, and scalable! ✅ Like, Share & Subscribe for more AI & ML Project Tutorials! 🤖🔔 🔔 Follow us for More Updates: 📸 Instagram: / pianalytix.official 🔗 LinkedIn: / pianalytix 🐦 YouTube: / @pianalytixofficial 🌐 Visit Pianalytix for More Resources: 🔗 Website: https://pianalytix.com Visit our website to access 300+ Data Science, Power BI, and Development projects. 📘 Explore Our Courses on Udemy: 📌 Build 45 Real-World Power BI Projects for BI & Data Analysts: https://www.udemy.com/course/data-ana... 📌 Data Science Bundle: 180 Hands-On Projects: https://www.udemy.com/course/data-sci... 📌 100 Days of Code: Real-World Data Science Projects Bootcamp: https://www.udemy.com/course/hands-on...