Movie Recommendation System | Python Machine Learning Project Tutorial for Beginners

Описание: A comprehensive tutorial on building a Movie Recommendation System with machine learning! In this beginner-friendly guide, you'll learn how to develop a robust recommendation system using Python, Pandas, Scikit-learn, and Streamlit.

Resources:
• Dataset: https://www.kaggle.com/datasets/tmdb/...
• Source Code: https://github.com/dutta-sujoy/Movie-...

Chapters:
Introduction to Movie Recommendation Systems.
Loading and Exploring Movie Data.
Data Cleaning and Preprocessing.
Implementing Recommender System Algorithms.
Integrating TMDB API for Movie Posters.
Evaluating and Fine-tuning the Recommendation Model.
Building a Streamlit App for Interactive Recommendations.

Tools and Technologies Used:
• Python: Core programming language for development.
• Pandas: Data manipulation and analysis.
• Scikit-learn: Machine learning library for building models.
• Streamlit: Framework for creating and deploying interactive web apps.

Why Watch This Tutorial?
By the end of this video, you'll have a fully functional movie recommendation system ready to showcase in your portfolio. Whether you're new to machine learning or looking to expand your skills in recommendation systems, this tutorial will provide hands-on experience and practical insights.

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