I have compared different classification models while predicting phishing websites rather than limiting myself to only one model. This gives us an idea about which model is best suited for a perticular requirements.
For Ex.- Let's say got best accuracy in MLP but best Precision in Decision Tree which is an important comparison because we don't want a model which may declare a phishing website as legitimate because that may prove to be dangerous for a user. But also, we don't want a model whose accuracy is low since we get very less output. What we need is a good amount of accurate output with good precision also.
- Loading the dataset
- Familiarizing with data
- Visualizing data
- Data Preprocessing and EDA
- Hold Out validation
- Model Building and Training (Decision Tree, Random Forest, MLP, XGB and SVM)
- Comparison of Models