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https://github.com/souravsuvarna/missnomore
https://github.com/souravsuvarna/missnomore
Last synced: 6 days ago
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- Host: GitHub
- URL: https://github.com/souravsuvarna/missnomore
- Owner: souravsuvarna
- License: mit
- Created: 2023-07-21T17:38:27.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-08-13T10:01:14.000Z (over 1 year ago)
- Last Synced: 2023-08-13T10:49:59.945Z (over 1 year ago)
- Language: Python
- Size: 30.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MissNoMore - Missing Value Imputation Tool for CSV Datasets
MissNoMore is a Python-based missing value imputation tool designed to handle CSV datasets with missing data. It offers a range of imputation techniques, from simple mean and median strategies to more advanced methods like K-Nearest Neighbors (KNN), iterative imputation, and decision tree-based imputations. The tool provides both a "Basic" mode for quick imputations and an "Advanced" mode for more sophisticated approaches.
## Demo
Try out the deployed MissNoMore application [here](https://missnomore.streamlit.app/).
## Features
- Missing value imputation for CSV datasets
- Two modes: "Basic" (mean, median, interpolate) and "Advanced" (KNN, iterative, decision tree)
- Easy-to-use Streamlit interface
- Leveraging the power of Pandas for data manipulation
- Improves data quality by handling missing data effectively
## Getting Started
### Prerequisites
- Python 3.8+
- Pandas library
- Streamlit library
- Scikit library
### Installation
1. Clone the repository:
```
git clone https://github.com/souravsuvarna/MissNoMore.git
cd MissNoMore
cd app
```
3. Install the required libraries:
```
pip install pandas
pip install streamlit
pip install scikit-learn
```
### Usage
1. Run the Streamlit app:
```
streamlit run app.py
```
2. Choose the CSV dataset with missing values you want to impute.
3. Select the desired imputation mode (Basic or Advanced).
4. For Basic mode, choose the column from drop-down list then choose appropriate imputation technique.(mean, median,interpolate,etc) .
5. For Advanced mode, appropriate imputation techniques.( KNN, iterative, decision tree-based imputations,etc).
6. Click the "Submit" button to process the data and generate the imputed dataset.
7. Download the imputed dataset for further analysis.
## Contributing
Contributions to MissNoMore are welcome! If you find any issues or have suggestions for improvements, please feel free to create a pull request or raise an issue.
## Acknowledgments
- The MissNoMore project was inspired by the need for a user-friendly missing value imputation tool.
- Thanks to the developers of Pandas, Streamlit, and other open-source libraries used in this project.
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