topic_modeling
Usage
- Basic Usage: Run
topic_modeling.exe
or usetopic_modeling_main.ipynb
- Configuration: Modify pyproject.toml to add or remove packages.
Data
- Sources: The dataset is collected by CPG distributor public site.
- Structure: Table of key features
Example
Input data format
Text |
---|
string |
Result ✅
- Findings:
-
Based on the model the results of topics and words are different. This is not surprising as these methods are using different approaches under the hood.
Overall, I would prefer to go with the LDA method as is has been a well stablished methodn on this field.
-
Visualizations:
- Example visualizations (if applicable).
Directory Structure
.
├── docs <- markdown files for mkdocs
│ └── img <- assets
├── notebooks <- jupyter notebooks for exploratory analysis and explanation
└── src - scripts for processing data eg. transformations, dataset merges etc.
│ ├── data <- loading, saving and modelling your data
│ ├── features <- feature engineering
│ ├── model <- algorithms and models
│ ├── plots <- plots
│ └── utils <- api and other
├── LICENSE <- License
├── mkdocs.yml <- config for mkdocs
├── pyproject.yml <- config project
└── README.md <- README file of the package
Contributing
To contribute create a PR a use conventional commits
fix: <description>
feat: <description>
docs: <description>
refactor: <description>
License
The project is licensed under the MIT License.
I hope this is helpful!