📄️ Introduction
This project was for the course Machine Learning Practice.
📄️ Data
The dataset used for the competition consisted of features indicating amount of time spent on various pages of the site by the visitor, personal details of the visitor such as gender, marital status and education, and OS/search engine being used by the visitor.
📄️ Preprocessing
The data was first cleaned and preprocessed to handle missing values, categorical features, outliers, class imbalance and redundant features.
📄️ Estimation
Various models were tried for this problem, with the exception of deep neural networks, since tensorflow and pytorch were forbidden for the project/competition.
📄️ Improvements
Since this was my first ever Kaggle competition and Machine Learning project, I was familiar with and could implement only the basics that I detailed. There were a lot more things I could have done.