PHISHCOOP -- Phishing Website Detection

Detection of phishing websites is a really important safety measure for most of the online platforms. So, as to save a platform with malicious requests from such websites, it is important to have a robust phishing detection system in place.

DATA SELECTION

The dataset is downloaded from UCI machine learning repository. The dataset contains 31 columns, with 30 features and 1 target. The dataset has 2456 observations.

MODELS

To fit the models over the dataset the dataset is split into training and testing sets. The split ratio is 75-25. Where in 75% accounts to training set.

Now the training set is used to train the classifier. The classifiers chosen are:

* Logistic Regression

* Random Forest Classification

* Support Vector Machine

We will see which one fits best in our dataset.

1.Logistic Regression

Fitting logistic regression and creating confusion matrix of predicted values and real values I was able to get 92.3 accuracy. Which was good for a logistic regression model.

2.Support Vector Machine

Support vector machine with a rbf kernel and using gridsearchcv to predict best parameters for svm was a really good choice, and fitting the model with predicted best parameters I was able to get 96.47 accuracy which is pretty good.

3.Random Forest Classification

Next model I wanted to try was random forest and I will also get features importances using it, again using gridsearchcv to get best parameters and fitting best parameters to it I got very good accuracy 97.26.

Random forest was giving very good accuracy. We can also try artificial neural network to get a improved accuracy.

FEATURE IMPORTANCES