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Nidhi-Satyapriya
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Feature Description
Develop a system to predict the popularity of books using historical sales data, reviews, ratings, and other relevant features. The system should leverage machine learning and deep learning models to provide accurate predictions and insights.
Goals:
Collect and preprocess relevant data for book popularity prediction. I will use https://www.kaggle.com/datasets/thedevastator/books-sales-and-ratings Engineer features from collected data. Select and train appropriate machine learning and deep learning models. Evaluate model performance Deploy the model for real-time predictions.
Use Case
Publishers, retailers, and authors can stay ahead of market trends, making more informed decisions and gaining a competitive advantage. Guides publishers in selecting and promoting books.
Benefits
By identifying potential bestsellers early, publishers and retailers can allocate their marketing budgets more effectively, targeting the right audience with tailored campaigns. Predicting which books will become popular enables better promotion and placement, leading to increased sales and higher revenue. Reduces the risk of overstocking or stockouts.
Add ScreenShots
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Priority
High
Record