https://github.com/natural-intelligence/data-science-interview-excercise
https://github.com/natural-intelligence/data-science-interview-excercise
Last synced: 11 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/natural-intelligence/data-science-interview-excercise
- Owner: Natural-Intelligence
- Created: 2019-12-11T05:49:58.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-01-15T11:31:23.000Z (over 6 years ago)
- Last Synced: 2024-12-27T04:24:54.500Z (over 1 year ago)
- Language: Python
- Size: 1.12 MB
- Stars: 0
- Watchers: 45
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
As a marketing company we are always looking for search terms that will show up in high position.
You are given a dataset of our different google campaigns, their keywords, and average position
campaignname keyword averageposition impressions
0 IDT US best id protection 1.5 2
1 IDT US identity protection services 4.0 4
2 IDT US credit monitoring services 2.7 356
3 IDT US identity theft protection 5.0 58
4 IDT US identity theft services 3.0 1
5 IDT US fraud protection services 6.0 1
6 IDT US +credit +monitoring +reviews 2.3 10
write a model that - given a campaign name, and an n-gram will return an estimated position or the n-gram.
### Example
for input (campaign: "IDT US", keyword: "best social security number prevention service") you might return average position 2.4
for input (campaign: "IDT US", keyword: "fraud service") you might return average position 7.3
## Guidelines
1) Write the code according to clean-code best practices.
2) Write a module that will be used by developers to estimate the positions of their ads,
Think about the usability of your API. how do you make it acessible to developers?
3) How would you test your code?