Create README.md

README.md

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+Data Description:
+Amazon Reviews data ( data source ) The repository has several
+datasets. For this case study, we are using the Electronics
+dataset.
+
+Domain:
+E-commerce
+
+Context:
+Online E-commerce websites like Amazon, Flipkart uses
+different recommendation models to provide different
+suggestions to different users. Amazon currently uses
+item-to-item collaborative filtering, which scales to massive
+data sets and produces high-quality recommendations in
+real-time.
+
+Attribute Information:
+● userId : Every user identified with a unique id
+● productId : Every product identified with a unique id
+● Rating : Rating of the corresponding product by
+the corresponding user
+● timestamp : Time of the rating ( ignore this column
+for this exercise)
+
+Learning Outcomes:
+● Exploratory Data Analysis
+● Creating a Recommendation system using real data
+● Collaborative filtering
+
+Objective:
+Build a recommendation system to recommend products to
+customers based on the their previous ratings for other
+products.
+
+Steps and tasks:
+1. Read and explore the given dataset. (Rename
+column/add headers, plot histograms, find data
+characteristics) - (2.5 Marks)
+2. Take a subset of the dataset to make it less sparse/ denser.
+( For example, keep the users only who has given 50 or
+more number of ratings ) - (2.5 Marks)
+3. Split the data randomly into train and test dataset. ( For
+example, split it in 70/30 ratio) - (2.5 Marks)
+4. Build Popularity Recommender model. - (20 Marks)
+5. Build Collaborative Filtering model. - (20 Marks)
+6. Evaluate both the models. ( Once the model is trained on
+the training data, it can be used to compute the error
+(RMSE) on predictions made on the test data.) - (7.5 Marks)
+7. Get top - K ( K = 5) recommendations. Since our goal is to
+recommend new products for each user based on his/her
+habits, we will recommend 5 new products. - (7.5 Marks)
+8. Summarise your insights. - (7.5 marks)
+
+References:
+● Recommeneder systems and its applications
+● Use cases of Recommendation systems