https://github.com/PriyanK7n/EC503-Project-An-Exploration-of-Recommendation-Systems

1. Books Recommendation Dataset
2. Artificial Dataset

• Python Programming language to perform experiments.
• Used Sklearn for Modelling SVD.
• Used Gridsearch for performing hyperparameter tuning.
• Used matplotlib library to build various plots to analyze and showcase the results of experiments.
• Pandas and Numpy to perform data-preprocessing

• Training and Testing Split: 80%, 20%
• num_of_components or top 'r' singular values =250
• iterations=20

• Ensured the Users have at least rated more than 300 books
• Ensured the Books had at least 50 ratings from users
• Fill Empty/NAN(Not a Number) values with zeros.
• Remove duplicates(ensures unique users and books)

• use load_data() function to load the book recommendation dataset
• use create_dense() to create an artificial dataset
• use plot_svd_iteration() to perform Experiment 1
• use increase_sparsity() and decrease_sparsity() to perform Experiment 2 on Book Dataset
• use increase_sparsity_artificial() to perform Experiment 3 on artificial dataset

1. https://github.com/iNeuron-Pvt-Ltd/Books-Recommender-System-Using-Machine-Learning
2. https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset
3. https://en.wikipedia.org/wiki/Singular_value_decomposition
4. https://towardsdatascience.com/recommender-system-singular-value-decomposition-svd-truncated-svd-97096338f361

├── LICENSE
├── README.md          <- The top-level README for developers/collaborators using this project.
│
│ 
├── src                <- Source code folder for this project
    │
    ├── data           <- Datasets used and collected for this project
    │
    ├── visualizations <- Folder to store and Visualization generated for the project

• Reports - Folder to store all Final Reports of this project
• Data - Folder to Store all the data collected and used for this project
• References - Folder to store any referenced code/research papers and other useful documents used for this project
• Visualizations - Folder to store plots
• Results - Folder to store Final results and code.