Phishing attacks are a form of social engineering that deceives users by imitating legitimate websites to steal sensitive information. This study focuses on phishing attacks tar geting local systems and introduces a newly developed attribute based dataset for detecting such attacks. The proposed dataset consists of 31 attributes derived from URL- and similarity based features. To assess the impact of feature engineering, three distinct datasets were generated, and their performance was compared across multiple classifiers. Several machine learning algorithms, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, and Artificial Neural Network, are applied to evaluate classification performance. Experimental results demonstrate high accuracy in detecting phishing attacks on local systems, with the Logistic Regression method achieving the best result of 96.40%. Furthermore, validation on a publicly available dataset from Kaggle yielded an accuracy of 97.48%, confirming the model's strong generalization capability. These findings highlight the effectiveness of attribute-based datasets combined with machine learning approaches for improving phishing detection in real-world environments.