This experiments consist of a ML model that predicts whether a website is a phishing one or not.
This experiments consist of a ML model that predicts whether a website is a phishing one or not. **Approach:** 1. Obtained Data set from https://archive.ics.uci.edu/ml/datasets/Phishing+Websites 2. Convert the data set into CSV format. 3. Select the required/important features ![Features importance] : https://drive.google.com/open?id=14smjsiRFbJx2d3SgXfOagxUEzsukrZux 4. Split the data set into training and testing data.(Training data-80% ,Testing Data-20%) 5. Train the model with training data using Two-class Decision tree Algorithm.(contains 30 trees). 6. use test data for prediction . 7. Find accuracy ,confusion matrix and other metrics and analyze the model. Current model gives ~96% accuracy using split-sample technique