If you are a certified data scientist, you probably have encountered some of these issues before. If you are a beginner, these use cases will help you learn different data science ideas that can be applied across the industry. Data science challenges are not evolving as quickly as possible for most organizations. Use cases will grow through many competitors depending on your planning needs and expectations. It is crucial to provide insights into current use cases so that they can be condensed and applied to new use cases. You'll occasionally encounter scenarios that haven't been written about in articles or studied at institutions. The allure of data science is that it is scalable and applicable to many issues while requiring relatively little effort.
1. Credit Card Fraud Detection
We'd create a supervised model to classify it as either fraud or not fraud in this situation. In an ideal world, you'd have many samples of what noise looks like in your data.
2. Customer Segmentation
Unsupervised learning would be preferred over classification to employ clustering in this circumstance. K-Means is a traditional clustering algorithm. This task is unsupervised because you don't possess labels and don't know what to group. However, you'd like to uncover patterns of novel combinations based on their shared points.
3. Customer Churn Prediction
A family of machine learning techniques could help with this problem. This query is similar to the one used to detect credit card fraud. We want to collect customer information with a specific label, such as churn or no-churn.
4. Sales Forecasting
Forecasting transactions is perhaps the most diverse of the three use cases discussed so far. We can apply deep learning to anticipate future commodities purchases in this example. The LSTM algorithm was utilized. LSTM stands for Long Short-Term Memory.
