(Data Mining Project)
- Build a Movie recommendation system based on “Association Rules”
- Use User Ratings data as the reference feature
- Create a website that gives Top 10 recommendations based on the movie selected
After thorough literature and technology survey, approach chosen for this project is as following:
- Use open-source Neflix user rating dataset from Kaggle (https://www.kaggle.com/datasets/netflix-inc/netflix-prize-data)
- Explore different FIM(Frequent Itemset Mining) Algorithms
- Compare all the models and choose best model
- Data Collection
- Data Exploration
- Data Preprocessing
- Data Transformation
- Model Development
- Evaluation
- Live demo
- Applications and Future Scope
Aneshaa Kasula, Viritha Vanama and Vishalakshi Ramanathan
This repository contains Jupyter notebooks focused on each stage of CRISP-DM such as data coolection, EDA visualizations,and so on using Python
- Yaman, Sezin & Fagerholm, Fabian & Munezero, Myriam & Männistö, Tomi & Mikkonen, Tommi. (2019). Patterns of User Involvement in Experiment-Driven Software Development. Information and Software Technology. 120. 106244. 10.1016/j.infsof.2019.106244.
- https://towardsdatascience.com/how-data-science-is-boosting-netflix-785a1cba7e45
- http://norma.ncirl.ie/4403/1/samruddhishaileshkanhere.pdf
- https://machinelearningknowledge.ai/best-explanation-of-apriori-algorithm-for-association-rule-mining/
- https://www.geeksforgeeks.org/depth-first-search-or-dfs-for-a-graph/
- https://www.youtube.com/watch?v=HV49RIUIx9Q
- https://www.youtube.com/watch?v=5ZOAJ6mn5Rg
- https://giphy.com/stickers/netflix-stream-streaming-binge-VD4svD2E2rMZXwuHkR