This paper describes the model submitted by the BFCAI team for the CoLI-Tunglish shared task held at FIRE 2023. The proposed approach employs character n-gram TF-IDF vectorization enhanced with word-length features. Several machine learning classifiers were evaluated, including Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), and Multi-layer Perceptron (MLP). Experimental results demonstrate that SVM outperformed all other models. System submissions were evaluated using the macro-average F1 score, where the proposed SVM-based model achieved an F1 score of 81.2% on the test set, ranking second among all participating teams