Phishing attacks are one of the most common and dangerous cyber threats, where attackers attempt to steal sensitive information such as usernames, passwords, and banking details by disguising malicious websites or emails as legitimate ones. With the rapid growth of online services, traditional rule-based security systems have become insufficient to detect evolving phishing techniques. This project focuses on building an intelligent phishing detection system using machine learning to accurately identify phishing websites and protect users from cyber fraud. The proposed system uses a publicly available phishing dataset containing both legitimate and phishing URLs. The dataset is preprocessed by handling missing values, removing irrelevant attributes, and converting categorical features into numerical form. Exploratory Data Analysis (EDA) is performed to understand feature distributions and correlations. To address the issue of class imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, ensuring better model learning and fair prediction performance. The final model effectively classifies URLs as phishing or legitimate with high accuracy and reduced false positives. This system can be integrated into web browsers, email filters, or organizational security platforms to provide real-time phishing detection. The project highlights the importance of machine learning in cybersecurity and demonstrates a scalable, data-driven approach to combating modern phishing attacks.

The proposed phishing detection solution will be developed using Python as the primary programming language due to its strong support for data science and machine learning. Data handling and preprocessing will be performed using Pandas and NumPy, while Scikit-learn will be used to implement machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes, along with evaluation metrics like accuracy, precision, recall, and F1-score. For data visualization and analysis, Matplotlib and Seaborn will be utilized to generate graphs and correlation plots. The dataset will be sourced from platforms like PhishTank and the UCI Machine Learning Repository.