Finding the discriminative power of features to analyse how different parameters affect the rating of a book

Motivation

Literature Review

Curating the dataset

Figure 1: Data distribution of(a) numerical data (b) genres and (c) language

Preprocessing

Figure 2: Preprocessing steps

Feature Selection

Methodology and Results

Figure 3: Methodology for linear regression models
Table 1: Regression results
Figure 4: Scatterplot of actual rating vs predicted rating for regression models
Table 2: Ensemble models performance
Table 3: Classification models performance
Figure 5: CNN structure for book covers
Figure 6: Actual rating vs predicted rating of neural networks on book covers
Figure 7: ANN structure for summary
Table 4: performance of models in predicting the rating of a book from its summary
Figure 8: Scatterplot for actual vs predicted ratings using summary from (a) ANN (b) ANN with regularisation

Discussion

Figure 9: Weights of features in linear regression for predicting rating from numerical data
Figure 10: Change in MSE on increasing the number of hidden units in the ANN for predicting rating from summary
Figure 11: Weights of some words in linear regression on Summary for predicting the rating of a book

Final Model

Figure 12: Final model architecture
Table 5: Performance of the final model as compared to the individual models
Figure 11: Scatterplot of actual vs predicted rating for the final model on testing data

Conclusion

References

Acknowledgements

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store