Phishing-website-detection Project Overview

Classified phishing websites using one-hot encoded parameters and compared the predicted result from Logistic Regression, Random Forest, XGBoost models. Improved accuracy from 85% to 95% with hyperparameter tuning with grid and random search.

Code and Resourcess used

Python version: 3.7 Packages: pandas, numpty, sklearn, matplotlib, seaborn, Other resources...

Data

Columns

Data Cleaning

After acquiring the data, I needed to clean it up so that it was usable for our model. I made the following changes and created the following variables:

EDA

Model Building

First, I transformed the categorical variables into dummy variables. I also split the data into train and tests sets with a test size of 20%.

I tried three different models and evaluated them using Mean Absolute error. I choose MAE because

The three different models I tried:

Model Performance

The random forest model far outperformed the other approcahes on the test and validaiton sets. RF: MAE =

Productionization