Business Data Problem : Bike shop Company
Part II : New product recommendation
Problem Statement
Research and Development wants help to determine new product ideas and pricing using existing product line as a benchmark.
Solution Summary
We’ve identified several product gaps in the existing product line including:
1. Aluminum Over Mountain
2. Aluminum Triathalon
The Data Science Team has developed a pricing model that uses predictive analytics to estimate the price of the new bicycle models based on the existing fleet. This ensures that new models are priced comparatively to other similar bicycles.
New product prediction for 2 new models:
1. Trigger, Over Mountain with Aluminum Frame: $2,985
2. Slice, Triathalon with Aluminum Frame: $2,438
Next Steps: Integrate the model into a proof-of-concept web application that can be deployed to the R&D department.
Gap Analysis
The visualization segments the full bicycle product line by category and frame material. This exposes two
product gaps:
1. New Aluminum line of bikes in the Over Mountain Category
2. New Aluminum line of bikes in the Triathalon
Price Prediction
I used an unsupervised ML algorithm (XGBOOST) to get a price prediction for the 2 new models :
1. Trigger, Over Mountain with Aluminum Frame: $2,985
2. Slice, Triathalon with Aluminum Frame: $2,438.
References
The repository of this project is on my github channel, where you can find the .pdf report (french and english) (link below)
→ https://github.com/dimsu/Product_Recommendation
Also, If you want to have a quick glimpse, I made an interactive report that you can find on my rpubs website here (link below)
