Use Machine Learning to Detect Phishing Websites

pandas, NumPy, scikit-learn, logistic regression, Weights & Biases

Sayak Paul

4 weeks · 5-8 hours per week

In this liveProject, you’ll take on the role of a data scientist employed by the cybersecurity manager of a large organization. Recently, your colleagues have received multiple fake emails containing links to phishing websites. Phishing attacks are one of the most common—and most effective—online security threats, and your manager is worried that passwords or other information will be given to an attacker. You have been assigned the task of creating a machine learning model that can detect whether a linked website is a phishing site. Your challenges will include loading and understanding a tabular dataset, cleaning your dataset, and building a logistic regression model.

project author

Sayak Paul

Sayak works at PyImageSearch where he applies deep learning to solve problems in computer vision, and brings solutions to edge devices. He also provides Q&A support to PyImageSearch readers. Previously, Sayak developed projects and practice pools for DataCamp. Outside of work, Sayak enjoys writing technical articles and giving talks at developer meetups and conferences.

Prerequisites

This liveProject is designed for developers interested in data science and for beginner data scientists. To begin this liveProject, you will need to be familiar with:

TOOLS

Basics of Python and its utility functions
Basics of pandas
Basics of NumPy
Basics of scikit-learn

TECHNIQUES

Basics of data science

you will learn

In this liveProject, you’ll learn to build a machine learning model using common Python libraries. You’ll develop techniques for querying datasets, data cleaning, performing hyperparameter tuning, and analyzing and summarizing the performance of your models. These skills can easily be applied to a wide variety of machine learning tasks and other data projects.

Loading and understanding tabular datasets using pandas
Preprocessing tabular datasets with NumPy
Preparing reports on your data with visualization tools
Creating a logistic regression classifier as a baseline model using scikit-learn
Using random searching to find optimal hyperparameters of the baseline model

features

Self-paced: You choose the schedule and decide how much time to invest as you build your project.
Project roadmap: Each project is divided into several achievable steps.
Peer support: Chat with other participants within the liveProject platform.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.
Book and video resources: Excerpts from Manning books and videos are included, as well as references to other resources.