Optimizing Revenue Generation in E-Commerce: An Analysis of Customer Purchase Intent Using Machine Learning Technique

In the rapidly changing world of electronic commerce, knowing and predicting what makes people buy things is key to making money. This research tests out how effective machine learning can be in analyzing customer data sets collected from e- commerce platforms. The researchers use a dataset gleaned from the UC Irvine Machine Learning Repository, which provides multiple predictors about user behavior during site visits as well as an outcome variable denoting whether they made purchases or not. Given that there are imbalances between different classes' representation rates within this sample size, our aim is developing classification models for purchase intentions while still considering such discrepancies [1]. In other words - through this project we want to find out what variables contribute most towards buying something online among all others available. The most appropriate algorithm turns out to be Random Forests due its stable performance over various runs where many decision trees get built simultaneously using different subsets of training data; then each tree votes independently so final predictions come from multiple paths thus reducing bias caused by using single model alone. When Hyperparameters are tuned, models tend generalize better by controlling complexity reduce overfitting improve predictive accuracy. This study concludes with suggestions for online firms, indicating how they can make use of machine learning findings in order to promote sales directed at specific customers and maximize profits. In general, such an investigation contributes towards furthering knowledge on data-based methods for comprehending revenue creation within electronic commerce settings.