Step	Description	Output
1. Planning & Objectives	Defined recommendation goals and KPIs with product and marketing teams.	Clear goals for personalized product/service suggestions.
2. Data Collection	Transaction history and product data Customer browsing and engagement data Ratings, reviews, and preferences	Comprehensive dataset capturing customer preferences.
3. Data Cleaning & Preparation	Removed noisy or duplicate data Engineered features for user-item interactions	Clean dataset ready for model training.
4. Model Building	Built collaborative filtering and content-based models for recommendations.	Model suggesting relevant products or content to users.
5. Visualization & Insights	Dashboards with recommended items for user segments Conversion rate analysis and engagement insights	Personalized recommendations with actionable insights.
6. Testing & Feedback	Model evaluation using accuracy and engagement metrics Refinements based on user feedback	Enhanced model accuracy and relevance.
Final Output	Fully functional recommendation system integrated with platform.	Increased user engagement Higher conversion rates Improved user satisfaction