This repository contains the final deliverables for the course CPE313 Advanced Machine Learning and Deep Learning. The project focuses on the design and implementation of a business intelligence system using geospatial data. The goal is to develop a comprehensive framework that leverages geospatial information to identify and assess potential business opportunities in specific regions or areas of interest.
- Sentiment Analysis: Utilizes sentiment analysis techniques to extract sentiment polarity from textual data such as customer reviews.
- Topic Modeling: Incorporates topic modeling algorithms such as Latent Dirichlet Allocation (LDA) to uncover latent themes and topics within textual data.
- Geospatial Analysis: Combines geospatial analysis with sentiment analysis and topic modeling to provide a holistic understanding of business opportunities, including customer sentiments, market trends, and geographic preferences.
- Python
- TensorFlow/Keras for deep learning models
- Scikit-learn for machine learning utilities
- TextBlob for sentiment analysis
- Matplotlib/Seaborn for data visualization
- Web development frameworks (Streamlit)
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Dataset: This folder contains the business.json file from Yelp dataset.
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Notebooks: This folder contains Jupyter notebooks used for data preprocessing, model training, and analysis. Each notebook is named descriptively to indicate its purpose or the stage of the analysis.
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Model Deployment: This folder holds files related to the development of the web application.
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Presentation: This folder contains video related to the model deployment demonstration.
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Documentation: This folder contains documentation file which is the research paper.
Canja, Tricha Maie | (qtmdacanja@tip.edu.ph)
Villanueva, Iris | (qilvillanueva@tip.edu.ph)
This project is licensed under the MIT License.