Recommendation-systems

DATA DESCRIPTION:

• author : name of the person who gave the rating
• country : country the person who gave the rating belongs to
• data : date of the rating
• domain: website from which the rating was taken from
• extract: rating content
• language: language in which the rating was given
• product: name of the product/mobile phone for which the rating was given
• score: average rating for the phone
• score_max: highest rating given for the phone
• source: source from where the rating was taken

Steps and tasks:

1. Import the necessary libraries and read the provided CSVs as a data frame and perform the below steps. • Merge the provided CSVs into one data-frame. • Check a few observations and shape of the data-frame. • Round off scores to the nearest integers. • Check for missing values. Impute the missing values if there is any. • Check for duplicate values and remove them if there is any. • Keep only 1000000 data samples. Use random state=612. • Drop irrelevant features. Keep features like Author, Product, and Score.
2. Answer the following questions • Identify the most rated features. • Identify the users with most number of reviews. • Select the data with products having more than 50 ratings and users who have given more than 50 ratings. Report the shape of the final dataset.
3. Build a popularity based model and recommend top 5 mobile phones.
4. Build a collaborative filtering model using SVD. You can use SVD from surprise or build it from scratch(Note: Incase you’re building it from scratch you can limit your data points to 5000 samples if you face memory issues). Build a collaborative filtering model using kNNWithMeans from surprise. You can try both user-based and item-based model.
5. Evaluate the collaborative model. Print RMSE value.
6. Predict score (average rating) for test users.
7. Report your findings and inferences.
8. Try and recommend top 5 products for test users.
9. Check for outliers and impute them as required.
10. Try cross validation techniques to get better results.
11. In what business scenario you should use popularity based Recommendation Systems ?
12. In what business scenario you should use CF based Recommendation Systems ?
13. What other possible methods can you think of which can further improve the recommendation for different users ?