Machine Learning Sentiment Analysis Gambling Detection

PLEASE READ CAREFULLY!! This is a quite simple/small classification project task especially for new comer in upwork and i need to complete this as soon as today, I am looking for an experienced Machine Learning engineer to assist with a sentiment analysis/classification project. The primary model used in this study is XGBoost, trained on a dataset of approximately 1,500 labeled samples for online gambling (judol) detection. The objective is not only to build and evaluate the XGBoost model but also to conduct a comprehensive comparison with several other traditional machine learning algorithms to demonstrate the strengths and performance advantages of XGBoost. The project should include a complete end-to-end workflow, starting from data preprocessing and exploratory data analysis (EDA), followed by feature engineering and model development. The comparison should be performed using appropriate baseline models such as Logistic Regression, Naive Bayes, Random Forest, Decision Tree, SVM, or other relevant algorithms. The final results should be presented in a clear I expect a thorough evaluation using multiple performance metrics, including but not limited to Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix analysis where applicable. In addition, I would like comprehensive visualizations and plots, such as class distribution analysis, feature importance, correlation analysis (if relevant), confusion matrices, ROC curves, performance comparison charts, and any other insightful visual representations that strengthen the findings. The deliverables should include well-documented and reproducible code, a clear explanation of the methodology, detailed interpretation of results, and professional quality visualizations. Experience with machine learning research projects, classification tasks, XGBoost optimization. Please include examples of similar machine learning or data science projects you have completed in the past when submitting your proposal.