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Problem: Develop product recommendation engine and detect bias based on data "electronics" and "modcloth" In Solution.ipnyb : 1.Install the required packages like Surprise, Numpy, Pandas, Matplotlib, etc 2.Import the data and convert the csv into dataframe. 3.Electronics Dataset: *Analyze the data based on its attributes like Popularity, Satisfaction, plot graphs for better understanding. *For Using Collabrative System for recommendation, use SVD Matrix Factorization and train the model. *Use Hybrid Recommendation for showing the best 5 recommendations for a particular product for a user. 4.ModCloth Dataset: *Analyze the data based on its attributes like Popularity, Satisfaction, plot graphs for better understandin *Model-Based Collaborative Filtering method using Surprise. *Applied multiple GridSearch on SVD with different n_factors and reg_all to get best parameters for our SVD's model performance. *Estimate and predict which Product the user would like. Attached published paper 'Addressing Marketing Bias in Product Recommendations.pdf' which I found online on ArVix shows that there is Marketing Bias with Product Selction as well as Consumer Satisfaction on both the datasets.
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