Use Machine Learning to Detect Phishing Websites
- prerequisites
- Beginner Python, Beginner scikit-learn, Basics of Data Science
- skills learned
- Work on tabular datasets with Pandas and NumPy, Build a logistic regression classifier, Use Machine Learning for security
Look inside
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.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
book resources
When you start your liveProject, you get full access to the following books for 90 days.
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
- 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.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
project outline
Introduction
Prerequisites Test
New module
Start Project
1. Loading and Understanding the Phishing Websites Dataset
1.1. Knowing the Dataset
A Quick Tour of Pandas
1.2. Submit Your Work
2. Further Data Investigation and Preparing Investigation Reports
2.1. Getting Useful Information from the Dataset
2.2. Submit Your Work
3. Cleaning the Class Labels and Inspecting for Missing Values
3.1. Cleaning the Class Labels and Inspecting for Missing Values
3.2. Submit Your Work
4. Training a Logistic Regression Model
4.1. Training a Logistic Regression Model
A Quick Primer on Logistic Regression
A Brief Take on Scikit-Learn
A Continuous Approach to Splitting Points: Logistic Regression
4.2. Submit Your Work
Summary
Project Conclusions
FAQs
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