Lilhore - Prof., Chitkara University Institute of Engineering and Technology, Chitkara University, ۱۴۰۴۰۱, Punjab, India.
Simaiya - Associate Prof., Chitkara University Institute of Engineering and Technology, Chitkara University, ۱۴۰۴۰۱, Punjab, India.
Prasad - Prof., Chitkara University Institute of Engineering and Technology, Chitkara University, ۱۴۰۴۰۱, Punjab, India.
Verma - Prof., Chhatrapati Shahu Ji Maharaj University. Kalyanpur, Kanpur Uttar Pradesh, India.

Due to enchantment in network technology, the worldwide numbers of internet users are growing rapidly. Most of the internet users are using online purchasing from various sites. Due to new online shopping trends over the internet, the seller needs to predict the online customer’s choice. This field is a new area of research for machine learning researchers. A random forest (RF) machine learning method is a widely used classification method. It is mainly based on an ensemble of a single decision tree. Online e-commerce websites accumulate a massive quantity of data in large dimensions. A Random Forest is an efficient filter in high-dimensional data to reliably classify consumer behaviour factors. This research article mainly proposed an extension of the Random Forest classifier named “Weighted Random Forests” (wRF), which incorporates tree-level weights to provide much more accurate trees throughout the calculation as well as an assessment of vector relevance. The weighted random forest algorithm incorporates the C۴.۵ method named a “Hybrid Weighted Random Forest” (HWRF) to forecast online consumer purchasing behaviour. The experimental results influence the quality of the proposed method in the prediction of the behaviour of online buying customers over existing methods.

Weighted random forest, Machine learning, Classification, Prediction, Online customer