Data Science – List of Common Machine Learning Problems with Examples

This article represents quick examples for 5 different classes of machine learning problems/tasks. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

Following is a set of 5 key machine learning problems/tasks whose examples have been listed later in this article:

  • Regression
  • Classification
  • Clustering
  • Association Rules
  • Artificial Neural Networks


Examples – Regression Models
  • Real Estate – Housing price estimation
  • Financial – Stock price estimation
  • Insurance – Estimate medical care expenses
  • Sales & Marketing – Sales vs Ad spend
  • Company growth estimation


Examples – Classification Models

Following are four different algorithms whose examples have been listed below:

  • Naive Bayes – Following are some of the examples:
    • Text classifications problems such as Spam filter, topic categorization
    • Diagnose medical conditions
    • Intrusion detection
  • K-Nearest Neighbors – Following are some of the examples:
    • Facial recognition (Computer vision)
    • Patterns identification in gene data
    • Cancer disease diagnosis such as Breast cancer
  • Classification trees – Following are some of the examples:
    • Credit scoring models
    • Marketing analysis of customer satisfaction
    • Diagnosis of medical conditions
  • Support Vector Machine (SVM) – Following are some of the examples:
    • Text categorization (organize document by topics)
    • Fraud analysis, security breaches identification
    • Cancer and other genetic disease identification based on classification of microarray gene expression
    • OCR


Examples – Clustering Models
  • Customer Segmentation: Segmenting customers into groups with similar demographics or buying patterns for targeted marketing campaigns
  • Anomalous Behavior Detection: Detecting anomalous behavior, such as unauthorized intrusions into computer networks, by identifying patterns of usage
  • Simplifying extremely large datasets by grouping a large number of features with similar values


Examples – Association Rule Models
  • Analysis of frequently occurring patterns: Used in various ecommerce sites. One such example is to identify items that are purchased together.
  • Patterns of purchases or medical claims in relation with fraudulent credit card or insurance use


Examples – Artificial Neural Networks (ANN) Models
  • Speech & handwriting recognition
  • Automated devices such as self-driven cars, self-driven drones
  • Weather and climate patterns
  • Social & economic phenomena
  • Tensile strength, fluid dynamics etc.


Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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