OMS is a group of services which basically deals with order processing. After confirmation of payment, Order details are passed to OMS. OMS verify payment fraud status using third part service Sift Science and send the order to specific vendors based on cart item’s vendor code. Then at the end, there is a tracking service which keeps customers updated about Order’s shipment and tracking details.
It predicts what a user may or may not like from the given list of items. Based on the user’s event history, it recommends the users the products he may like to purchase. It is both user based and item-to-item based recommendations, it will take account of both the user’s activities and the item’s attributes to generate the recommendations.
It is an application to find the shortest route between the user’s location and the warehouse’s location having enough stock of the requested item. The list warehouses having enough stock for that item is determined. Afterward, the user’s zip code along with the warehouse’s zip code is converted to longitude and latitude values. The distance between the user’s location and all warehouse’s location are calculated using haversine distance and the minimum distant warehouse is determined and its shipping charges are calculated.
Sales predictor is a tool that consists of a recurrent neural network trained on time series data to predict sales based on the number of items sold in the past. It takes into account of the time as its 3rddimension.
Solution :-
The goal was to find out the region with the maximum number of products sold. Based on the geo-coordinates of a particular point and the radius i.e. the maximum distance that two points can have to be included in the cluster is provided to the algorithm and it can then generated the clusters with varying densities based on the number of products sold.
Weather plugin is a tool that outputs weather information of a particular location based on it latitude and longitude values using OpenWeatherMap Rest API. The information is then cached using Apache Ignite caching services for faster access.
Solution
Prepared catalog data ofan e-commerce website using Apache Spark, handled missing information and uploaded the data into DSE Graph for visualization and analytics. The data is saved to Cassandra which is then indexed using Solr.
The project aimed at creating a Natural Language Processing API for Big Data using PySpark and Flask. The API was to be called from front-end and the results of NLP transformations like stemming, lemmatization, stop words removal, tokenization, Document Term Matrix, Sentiment Analysis, metadata extraction, etc were returned back.
How it will work :-
This project was aimed at boosting the e-commerce product sales. The e-commerce portal contains several products in various categories and subcategories. The major idea was to find a find the regions of importance for a particular categories of sales and their intrinsic attributes by using an unsupervised approach.
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