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
Menterns at work
4
Level
Intermediate
Time Commitment
60 Hours
Duration
60 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.
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
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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.
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
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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
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What you’ll need before starting
Python Libraries.
Experience with Recommendation Algorithms
Experience with Recommendation Algorithms
Why Do Menternships?
Each Menternship experience is powered by the MentorMind experiential learning model. We empower you as a Mentern to produce your own unique real work output through mentoring, deep-learning & applying your learnings to work.
All our Menternships are co-created with companies. They are designed for real-time work in the corporate function which allows all levers of a work experience to flourish in your Menternship experience.
You get access to a guild of industry mentors with remarkably diverse experience in their fields. Mentors help you with expert advice, domain guidance or to understand a specific task better. Moreover, you can always depend on our community of menterns, MentorMind Garage, to seek assistance from your co-menterns, guides & mentors!
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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.
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Get Guidance in One-on-One Mentor Hours
Ask Questions in our Biweekly AMA
Resolve all doubts on Mentor Support Chatbox
Get Actionable Mentor Feedback on your work
Learn & Network at the exclusive Mentern Perks Club
