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Real-time Database Sync
Machine Learning / Data Mining / Text Mining

Machine Learning Analysis of Classification Models and Text Mining on Furniture Review Data

This technical machine-learning report demonstrates the practical application of predictive modelling and text mining using WEKA. The work is divided into two major tasks. The first evaluates and compares Support Vector Machine and Decision Tree classification models, while the second applies text-mining techniques to furniture-review data and compares multiple classifiers after preprocessing, feature selection and class balancing. The first task uses the Screenshots.arff dataset to investigate the performance of libSVM and J48 Decision Tree classifiers. A 70% training and 30% testing split is applied, and the models are manually tuned to examine how different parameter settings affect predictive performance. For libSVM, an RBF kernel is used while different gamma and cost values are tested through grid-search-style experimentation. The report identifies gamma 0.03 and cost 2 as the strongest tested combination, producing approximately 91.67% accuracy on the test split. The J48 model is also optimised by adjusting the confidence factor used for pruning. Several confidence-factor values are examined, with 0.09 producing the strongest reported result of 80% accuracy. The optimised SVM and J48 models are then compared using five-fold cross-validation, where libSVM achieves 89.75% accuracy compared with 81.25% for J48. The second task focuses on text mining of Furniture Reviews. Text preprocessing includes TF-IDF term weighting, stopword removal, stemming, conversion to lowercase and word-count generation. The resulting textual dataset is transformed into a numerical feature representation suitable for machine-learning classification. Dimensionality reduction is performed using InfoGainAttributeEval with Ranker, selecting the 900 most informative attributes. The dataset is then balanced using WEKA techniques including Resample and SpreadSubsample to reduce class bias before classification. Finally, three classifiers—Naive Bayes, libSVM and J48—are evaluated on the balanced text dataset. The reported accuracies are 90.52% for Naive Bayes, 58.62% for libSVM and 78.45% for J48. The analysis concludes that Naive Bayes performs strongest for the processed furniture-review dataset, while the wider exercise demonstrates the importance of preprocessing, parameter tuning, feature selection, class balancing and appropriate model evaluation in producing reliable classification results. Important: this upload appears to be the completed student report, not the actual assessment guideline. Because the document does not state the university, module name, academic level, academic year, required word count or prescribed referencing style, I would leave those fields as Not specified rather than guessing.

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Computer Science / Machine Learning

Machine Learning Portfolio Challenge: Support Vector Machines and Kernel Methods

This machine learning portfolio challenge requires students to demonstrate both theoretical understanding and practical application of machine learning methodologies introduced during the second block of the module. During Weeks 7–12, students select one of the machine learning methods covered in class and apply it to a dataset of their choice. The completed work contributes to an assessment portfolio and must demonstrate a clear, systematic and professionally documented experimental process. The accompanying learning material places particular emphasis on Support Vector Machines (SVMs) and kernel-based machine learning. SVMs are presented as maximum-margin classifiers that construct a decision hyperplane between classes, with support vectors playing the key role in defining the classification boundary. The material also introduces soft-margin optimisation, slack variables, the regularisation parameter C, primal and dual formulations, and the use of KKT multipliers. SVMs are additionally discussed in relation to multiclass classification through approaches including one-versus-all and Error Correcting Output Codes (ECOC). Kernel methods extend these principles by replacing explicit feature transformations with similarity functions. Students encounter concepts including Gram matrices, feature mappings, Mercer conditions and the kernel trick, alongside common kernel choices such as linear, radial basis function and polynomial kernels. The material demonstrates how kernel methods can represent nonlinear decision boundaries in the original input space while retaining a linear representation in an embedded feature space. The final submission must be produced as a single PDF lab notebook. It should contain clear and well-commented MATLAB, Python or equivalent code explaining each methodological step, experimental results presented through appropriate tables and/or plots, and narrative discussion explaining the selected approach, observations and conclusions. The notebook should integrate code, outputs and written explanation into a coherent and professional submission. Students may also optionally present their solution in class, where the quality of explanation and discussion can contribute additional marks.

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