Phishing Website Detection System | End-to-End Data Science Project with Python & ML | Codejay

Embark on a comprehensive journey to build a Phishing Website Detection System using Python and Machine Learning. This tutorial guides you through every step of the process, from data collection and preprocessing to model training, evaluation, and deployment using Flask. Ideal for data science enthusiasts and professionals aiming to enhance their portfolio with a real-world project. Dataset: https://www.kaggle.com/datasets/taruntiwarihp/phishing-site-urls What You’ll Learn: Introduction to Phishing Detection: 1. Understanding the significance of phishing detection in cybersecurity. 2. Overview of machine learning approaches to identify phishing websites. Data Collection & Preprocessing: 1. Gathering datasets containing legitimate and phishing URLs. 2. Cleaning and preparing data for analysis. Feature Engineering: 1. Extracting relevant features from URLs (e.g., length, presence of IP address, special characters). 2. Understanding the impact of different features on model performance. Model Training & Evaluation: 1. Implementing algorithms like Decision Trees, Random Forest, and XGBoost. 2. Evaluating models using metrics such as accuracy, precision, recall, and F1-score. Deployment : 1. Building an interactive web application for real-time phishing detection. 2. Deploying the model to a user-friendly interface. Why Watch This Video? Gain hands-on experience in building a complete machine learning project. Learn to deploy models in real-world applications. Enhance your portfolio with a practical cybersecurity project. Join the Community! If you find this tutorial helpful, please like the video and subscribe for more content on data science and machine learning. Don't forget to hit the notification bell 🔔 to stay updated on new releases! Let’s Connect: Follow us on Social Media for updates, exclusive content, and more. Join the Discussion: Share your thoughts, questions, and suggestions in the comments! 🔗 Helpful Links: LinkedIn: https://www.linkedin.com/in/jayprakashbind/ GitHub: https://github.com/codejay411 Instagram: https://www.instagram.com/jaypr4 Instagram: https://www.instagram.com/codejay23/ Tags: Phishing Detection, Machine Learning Project, Data Science Tutorial, Python, Cybersecurity, URL Feature Engineering, Decision Tree, Random Forest, XGBoost, Model Deployment, Real-Time Detection, End-to-End Project, ML for Beginners, Data Science Portfolio Hashtags: #PhishingDetection #MachineLearning #DataScience #Python #Streamlit #CyberSecurity #MLProject #AI #EndToEndTutorial #WebSecurity