https://github.com/jeffandyalltogether/mlrecommendationsystem

project code for a recommendation system for Amazon using collaborative filtering, ranking, and matrix factorization to enhance customer satisfaction and product discovery.
https://github.com/jeffandyalltogether/mlrecommendationsystem

eda matplotlib pandas python scikit-learn seaborn tensorflow

Last synced: about 2 months ago
JSON representation

project code for a recommendation system for Amazon using collaborative filtering, ranking, and matrix factorization to enhance customer satisfaction and product discovery.

• Host: GitHub
• URL: https://github.com/jeffandyalltogether/mlrecommendationsystem
• Owner: JeffandyAllTogether
• Created: 2024-12-06T13:44:26.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-06T14:40:15.000Z (about 2 months ago)
• Last Synced: 2024-12-06T15:34:34.731Z (about 2 months ago)
• Topics: eda, matplotlib, pandas, python, scikit-learn, seaborn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 4.78 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
Objective: Built a recommendation system for Amazon using collaborative filtering, ranking, and matrix factorization to enhance customer satisfaction and product discovery.

Approach: Utilized models such as User-User and Item-Item similarity, along with an optimized SVD matrix factorization model to recommend products based on user ratings and preferences.

Skills and Tools: Python, TensorFlow, Scikit-learn, Pandas, EDA with Matplotlib, Seaborn, and model tuning for optimal accuracy and recall.

After running and tuning models, I recommended the SVD model for Amazon due to its superior precision and accuracy, enhancing customer satisfaction and reinforcing Amazon’s position as a go-to platform for product discovery.