Make your AI portfolio standout with these projects.
Data science is becoming hyper-competitive. One way to stand out is with a portfolio of valuable projects —not just analyzing the Titanic and Iris species.
Here, we’ll cover six projects to boost your portfolio:
Churn is when a customer quits a service. In other words: It’s when companies lose money.
When Blockbuster’s customers left for competitors like Netflix, they churned. Churn analysis could have uncovered the why and potentially saved the once-dominant media giant.
The goal of churn analysis is to effectively pinpoint the causes of churn and fight it. By directly improving the bottom-line, churn analysis is a powerful AI use-case.
The first step to churn analysis is creating a dataset (or simply finding the relevant data). Kaggle offers many churn datasets — a popular one is the Telco Customer Churn dataset, though this is synthetic and not as unique.
For any of these datasets, you can easily download them as a CSV file, and upload them to Apteo to find insights (after making an account).
Customer segmentation lets you uncover the characteristics of different customers, enabling better targeting.
It’s a way to “organize” your customers, and understand the habits of these groups.
E-Commerce analysis is a great AI use-case, given the wealth of data available. You can identify browsing behavior that leads to increased sales, segment profitable users, identify pages likely to convert, and more.
People analytics is used to analyze and reduce employee turnover, spot opportunities for improvement, and more.
As LinkedIn reports, “for each employee lost, the cost to the company could be 50%–250% of his/her annual salary.” While that’s a big range, there’s no doubt that employee attrition is extremely expensive.
Intuitively, we can guess that things like overtime, low salary, poor performance, and living far from the office might impact attrition.
With people analytics, we can plug-in the data to quantify the true impact of these attributes, and more, on attrition. With the insights we get, we can take steps to reduce attrition, whether it’s reducing over-time, increasing salaries, changing department structures, or altering company policy.
Lead scoring helps teams close deals and build relationships by prioritizing customers likely to convert.
Whether you have 130 or 130,000 leads, you need to put your efforts where they matter.
Combating fraud is an age-old problem. Back in 1992, Sprint was using real-time fraud detection built by Symbolics, an AI company. Today, companies like Visa, PayPal, and FirstAm deploy data-driven fraud detection.