Data Science
Build a system to recommend products or content to users based on the historical clickstream data
Imagine a system that can predict what your users want to see or buy next, based on their previous interactions. This experience takes you on a journey from raw data to a fully integrated solution.
Certified by
Role
Data Scientist
Industry
Healthcare Diagnostics
No. of Subscribers
5
Level
Intermediate
Time Commitment
60 Hours
Duration
30 days
Tools you’ll learn
Here’s What You Work On
About the Company
Shyft is a Lifestyle management and tracking application for specific health conditions like chronic ailments, pregnancy care, chronic pains and more.

They offer multiple different health programs that are specifically catered for the chosen health conditions.
More than 30,000 customers trust Shyft to manage their chronic and health conditions.
Explore
the following work techniques
User Behaviour Analysis
Recommendation System Development
Model Formation & Evaluation
Bridging the gap
The primary purpose of this work experience project is to develop a robust recommendation system that enhances user engagement and satisfaction on an online shopping platform. 

By leveraging historical clickstream data, the system aims to predict and suggest products or content that align with the users' preferences and behaviors, ultimately driving higher conversion rates and improving the overall shopping experience.
Apply
the following skills
Exploratory Data Analysis (EDA)
Model formulation
User Experience Research & Analysis
Expected output
As an outcome to this experience, you are required to submit your recommendation model, along with a business presentation that showcases your analysis and model application.
Create
the following deliverables
Report on Application of Data Science in E-Commerce
Segmentation Report on User Behaviour
Business Presentation on findings and recommendation model
What you’ll need before starting
Python Libraries.
Experience with Recommendation Algorithms