<div> Machine Learning versus Deep Learning for Public Financial Report Classification and Popularity Prediction:  </div> <div> A Feasibility Study on Ghana's Controller and Accountant-general's Department </div>

Government agencies increasingly publish financial reports online, but small public-sector portals rarely have the labeled data needed to test automated classification tools. This study evaluates the feasibility of these tools using a corpus of 131 official reports from Ghana's Controller and Accountant-General's Department (CAGD). We compare classical machine learning models (Logistic Regression, Random Forest, and eXtreme Gradient Boosting [XGBoost] with Synthetic Minority Over-sampling Technique [SMOTE]) and character-level deep learning models (bidirectional Long Short-Term Memory [LSTM] and Text Convolutional Neural Network [TextCNN]) across three tasks: category prediction, binary popularity classification, and log-download forecasting. Models were evaluated using a stratified 70/15/15 train, validation, and test split, randomized hyperparameter search, and a final finetuning phase. The fine-tuned XGBoost classifier achieved 1.00 accuracy on the five-class category task. Logistic Regression reached 0.75 accuracy (Area Under the Receiver Operating Characteristic curve [ROC-AUC] of 0.80) on the binary popularity task, and Random Forest reached an R-squared of 0.85 on log downloads. An ablation study removing twenty-five keyword features dropped category accuracy from 1.00 to 0.80, a stable drop across five randomized data splits. This indicates keyword features inflated initial results, though remaining performance confirms the models learn nontautological structure. This work serves as an honest feasibility check, showing that thorough feature ablation is essential before deploying these models on live government portals.