Analysis for customer revenue generation

• Author(s) / Creator(s)

  • Kagdi, Mohmed Hasim

• Most businesses around the world have started using e-commerce. When using the e-commerce platform, it becomes easier to gather data about the users of the platform. Businesses must utilize this data collected to the best of their ability. This would result in the growth and survival of the businesses. In this project, we are going to discuss some reasons why businesses would want to invest in predicting the future using machine learning, learn the process of data mining, and identify and discuss a few different type of classifiers which can be used in prediction. To demonstrate how machine learning and different classifiers work, a dataset is acquired from the internet and analysis was performed on it. Using the analysis, a conclusion on the state of the website was prepared. The improvements the business could make to generate more revenue were identified and stated. Classifiers were used on the dataset after it was split 70-30 (train and test respectively) to check the performance of the classifiers.

• Date created

  2022

• Subjects / Keywords

  • classifiers
  • analysis
  • k-nearest neighbor
  • KNN
  • support vector machine
  • Gaussian Naive Bayes
  • logistic regression
  • random forest
  • gradient boost
  • ada boosting
  • revenue

• Type of Item

  Research Material

• DOI

  https://doi.org/10.7939/r3-zqc0-bv60

• License

  Attribution-NonCommercial 4.0 International