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https://github.com/3rd-son/streamlit-web-app
A wep app for EDA, Vizualization and model building
https://github.com/3rd-son/streamlit-web-app
machine-learning matplotlib pandas python seaborn sklearn streamlit
Last synced: about 1 month ago
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A wep app for EDA, Vizualization and model building
- Host: GitHub
- URL: https://github.com/3rd-son/streamlit-web-app
- Owner: 3rd-Son
- Created: 2023-06-30T11:29:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-01T15:17:58.000Z (over 1 year ago)
- Last Synced: 2023-08-17T16:57:56.517Z (over 1 year ago)
- Topics: machine-learning, matplotlib, pandas, python, seaborn, sklearn, streamlit
- Language: Python
- Homepage:
- Size: 325 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Web App for EDA, Visualization, and Classification
This web app is built using Streamlit and provides a user-friendly interface for performing Exploratory Data Analysis (EDA), visualizations, and training classification models on your dataset. It allows you to upload your own dataset, explore its features, visualize relationships, and evaluate various classification algorithms.
## Features
The web app offers the following features:
1. **EDA**: Perform Exploratory Data Analysis on your uploaded dataset, including summary statistics, missing values, and data distribution.
2. **Visualization**: Generate visualizations such as pair plots to explore relationships between features, histograms, scatter plots, and more.
3. **Model Training and Testing**: Train and test classification models on your dataset using the following algorithms:
- K-Nearest Neighbors (KNN)
- Support Vector Classifier (SVC)
- Naive Bayes
- Logistic Regression
- Decision Tree
4. **About Me**: Get to know the developer behind this web app. The "About Me" section includes links to their GitHub, LinkedIn, and Twitter profiles.
## Installation
To run this web app locally, follow these steps:
1. Clone the repository: git clone https://github.com/Vic3sax/streamlit-web-app.git
2. Change into the project directory: cd streamlit-web-app
3. Install the required dependencies using pip: pip install -r requirements.txt
4. Run the Streamlit app: streamlit run app.py
5. Open your browser and visit `http://localhost:8501` to access the web app.
## Dependencies
The following libraries are used in this project:
- Streamlit
- Pandas
- Scikit-learn
- Matplotlib
You can install them by running the following command: pip install -r requirements.txt
## Usage
1. Launch the web app by running the `app.py` file with Streamlit.
2. Upload your dataset using the provided file upload feature.
3. Explore the EDA section to gain insights into your dataset.
4. Navigate to the Visualization section to generate visualizations based on your data.
5. Proceed to the Model section to select and train classification algorithms on your dataset.
6. Evaluate the performance of the trained models and compare their results.
7. If needed, repeat the above steps with different datasets or tweak the model parameters.
## Contributing
Contributions to this project are welcome! If you encounter any issues, have suggestions, or would like to add new features, please submit an issue or a pull request.
## License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
## Contact
For any questions or inquiries, please reach out to me via the following channels:
- [](https://twitter.com/SaxVictory)
- [](https://www.linkedin.com/in/victory-nnaji-8186231b7/)
- [](https://github.com/Vic3sax)