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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.

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

  2. 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.