Netflix Ad Recommendation System
Netflix Ad Recommendation System is a machine learning project aimed at enhancing ad personalization for Netflix users. By analyzing user behavior, preferences, and watch history, this system delivers targeted ads that balance user satisfaction and advertiser goals.
Overview This project focuses on improving Netflix’s ad recommendation system using machine learning algorithms to enhance personalization and user engagement. By analyzing user behavior, preferences, and watch history, the system delivers highly targeted and relevant ads, ensuring an optimal balance between user satisfaction and advertiser needs. Features - Personalized Ad Recommendations: Leverages collaborative filtering and NLP techniques to predict and display relevant ads. - Dynamic Ad Insertion: Seamlessly integrates ads into natural content breaks without disrupting the viewing experience. - Real-time Data Processing: Continuously analyzes user behavior to update ad recommendations in real-time. - A/B Testing: Includes functionality to test multiple ad formats and placements for performance optimization. Tech Stack - Backend: Python, Jupyter Notebook - Machine Learning: Scikit-learn (KNN Algorithm) - Data Processing: Pandas, Numpy, Python String Collections - Database: MySQL - Deployment: Docker, AWS - Visualization Tools: Power BI, matplotlib - Prototyping: Figma for UX design Key Milestones - Data Collection: User data ingestion pipeline using Python-based data processing. - Model Training: Machine learning models (e.g., collaborative filtering, KNN, NLP) trained on user data using Scikit-learn and Python libraries. - Integration: Backend APIs for serving recommendations, integrated with user-facing platforms. - Testing: Evaluating model performance using Python tools like Jupyter and pandas. Key Visualized Metrics Include: - Subscription Plan Insights: A breakdown of users by subscription type (Premium, Standard, Basic) to identify customer segments and optimize ad placements accordingly. - Geographic Distribution: A map highlighting user locations across the United States, facilitating location-based ad targeting to improve relevance and ad campaign effectiveness. - Payment Method Analysis: Insights into total payments by method, offering valuable data for aligning payment preferences with targeted promotions and partnerships. - Genre-based User Engagement: Analysis of preferred genres (e.g., Documentary, Drama, Comedy) to tailor ads based on content consumption patterns, increasing the likelihood of ad engagement. Analysis This data-driven approach allows for an optimized ad recommendation strategy, ensuring Netflix delivers high-impact, relevant advertisements that align with both user preferences and advertiser goals.