Design of Business Intelligence on Geospatial Data Using Deep Learning

Overview

This project focuses on leveraging geospatial data and deep learning algo, and 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.

Technologies

• Python
• TensorFlow/Keras for deep learning models
• Scikit-learn for machine learning utilities
• Deep Learning Techniques: CNN, LSTM, BiLSTM, LDA, LSA
• Matplotlib/Seaborn for data visualization
• Data Processing Tools: Tokenizer
• Data Sources: Yelp Open Dataset
• Evaluation Metrics: Accuracy, Precision, Confusion Matrix, Coherence Score
• Geospatial Analysis Tools
• Natural Language Processing (NLP) Tools: textblob
• Web development frameworks (Streamlit)

Key Features

1. Data Collection and Preprocessing
2. Deep Learning Model Implementation
3. Performance Evaluation
4. Business Opportunity Analysis
5. Decision Support System
6. Contribution to Economic Growth

Folder Structure

• Dataset: This folder contains the business.json file from Yelp dataset.

• 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.

• Model Deployment: This folder holds files related to the development of the web application.

• Presentation: This folder contains video related to the model deployment demonstration.