Structured Query Language Injection Attack (SQLIA) is one of the most prevalent cyber attacks against web-based application vulnerabilities; that are manipulated through injection techniques to gain access to restricted data, bypass authentication mechanisms, and execute unauthorized data manipulation language. There are several solutions and approaches for identification and prevention of SQLIA, such as Cryptography, Extensible Markup Language (XML), Pattern Matching, Parsing and Machine Learning. Machine Learning (ML) approach has been found to be profound for SQLIA mitigation, which is implemented through defensive coding approach. Machine Learning Approach requires a lot of data for efficient model training with capability for using several attack patterns. ML approach can be used to mitigate a very hard blind SQL injection attack. An experimental analysis was performed in Waikato Environment for Knowledge Analysis on Logistic Regression (LRN), Stochastic Gradient Descent (SDG), Sequential Minimal Optimization (SMO), Bayes Network (BNK), Instance Based Learner (IBK), Multilayer Perceptron (MLP), Naive Bayes (NBS), and J48. Hold-Out (70%) and 10-fold Cross Validation evaluation techniques were used to evaluate the performance of the supervised learning classification algorithms to choose the best algorithm. The results of Cross Validation technique showed that SMO, IBK and J48 had Accuracy of 99.982%, 99.995% and 99.999% respectively; while Hold-Out technique showed that SMO, IBK and J48 had Accuracy of 99.986 %, 99.989 % and 100 respectively. On the other hand, in Cross Validation technique SMO, IBK and J48 had time to build model value of 10.15sec, 0.06sec, and 14.12sec respectively while in Hold-Out technique SMO, IBK and J48 had time to build model value of 9.71sec, 0.16sec and 14.28sec respectively. From the findings, IBK had the minimum time to build model in Cross Validation technique in addition to better performance in Accuracy, Sensitivity as well as Specificity and was chosen as the classifier for SQLIA detection and prevention. Therefore, beyond Accuracy, other performance evaluation metrics are critical for optimal algorithm selection for predictive analytics.