Amazon Product Recommendation using Network Analysis and Machine Learning

CS5664_TextPreProcessing_MetaData.ipynb contains:

1. Parsing the metadata and produce essential pandas dataframes.
2. Creating product-copurchase edge lists.

CS5664_NetworkAnalysis.ipynb contains:

1. Network information such as degree centrality analysis.
2. Node degree distribution.
3. Powerlaw and heavy tail distribution.

CS5664_MetaData_and_ProductReviews_Analysis_EGO_Graph_Recommendations.ipynb contains:

1. Text preprocessing.
2. Meta data and Product Review processing.
3. Sentiment Analysis.
4. Topic Modeling.
5. EgoGraph based Product Recommendations using product Titles.

CS5664_Product_Recommendations_MachineLearning.ipynb contains:

1. Review rating analysis.
2. KNN based SVD experimentation.
3. Hyperparameter tuning and training SVD with best params.
4. Product recommendations based on reviews.

Datasets:

• Appliances.json - Reviews information for electronic appliances.
• Magazine_Subscriptions.json - Reviews information for magazines.
• amazon-books.csv - information on products (books) generated from amazon-meta.txt.
• amazon-books-copurchase.edgelist - information generated from amazon-meta.txt contains purchase-copurchase similar edgelist.
• amazon-meta.txt - Data downloaded from SNAP.
• products_copurchases_links.csv - Purchase Copurchase list for all products generated from amazon-meta.txt used for network analysis.
• products_data.csv - contains all product information.

Supplementary Materials & Visualizations folders include:

1. Network Analysis plots.
2. WordClouds - Reviews, Sentiments, Topics.
3. Sentiment Analysis plots.
4. LDA topics.