Design An Online Grocery Recommendation System with ML

Everyone wants their life to be easier with automatic recommendations with customization. Isn’t it cool whenever you purchase your monthly grocery you get recommendations based on what you have saved in your cart? Of course, yes. Here Machine Learning comes into play. Whether it is your favourite music app recommendation what you would like to play next to Netflix or Amazon what you want to see next, Machine Learning works like magic in these cases.

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What Is Product Recommendation System?

A product recommendation system is a tool that is designed to provide suggestion to customers for what he would want to buy next. There are basically three types of product recommendation.

1. Based on the user’s personal preferences or what he/she has added in their carts or what he/she has seen.
2. Based on similar people’s choice. People of his age or sex or background.
3. By categorizing products. For instance, if a person has bought bread it is liked he would like to buy butter, egg or milk.

Concepts Used:

1. Sound knowledge of Python programming language (preferably Python 3 or above)
2. Some basic machine learning algorithms

Hardware and Software Used:

1. Your preferred OS (it can be a mac, Linux or Windows)
2. Desktop or laptop whatever you have
3. Enough RAM should be present in your laptop or PC to run the computations without any interruptions.
4. Python 3 or above installed in your device.
5. Some basic python libraries installed in your system

Implementations:

1. First and the most important thing that you need is data. Data and customers and products on sale.
2. After collecting the data, it has to be cleaned and filtered in order to get a better model and better predictions.
3. Check for the rows and columns. Remove all the data that you don’t need. Remove the fields that are incomplete. As this will result in wrong predictions.
4. There are different recommendation systems out there in the market and they use different Machine Learning algorithms. Algorithms include content-based filtering (CBF), collaborative filtering, complementary filtering etc.
5. When a user enters a website or installs the app for the first, the agency has no information about the user of what he will like or what he is trying to buy. There we have to apply a popularity-based strategy. Show the user all the trending products available. This will narrow down the choices of the user. The website will also get to know the location of the user, from which website he came from etc.
6. Recommendations depend on products rated by customers. But not all customers rate the products they have bought. This results in a sparse user-item matrix. In these cases, apply collaborative filtering with Naïve-Bias.
7. The collaborative method uses a nearest-neighbour algorithm to identify products. 

Conclusion:

First of all, you need to know the type of customer. Whether he is first on the website or he is a regular customer. Applying the methods will solely depend on your customer type. However, to get the most impact on your business, you need to build an accurate model to get the most out of it.

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• Python

• Machine Learning

Online Grocery Recommendation using Machine Learning