{"id":15063714,"url":"https://github.com/rishav-raj-sinha/data_transformer","last_synced_at":"2026-01-02T09:20:40.605Z","repository":{"id":256915256,"uuid":"856805287","full_name":"Rishav-Raj-Sinha/data_transformer","owner":"Rishav-Raj-Sinha","description":"GUI based data transformation web-app","archived":false,"fork":false,"pushed_at":"2024-09-15T15:53:27.000Z","size":99,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-22T08:37:25.494Z","etag":null,"topics":["data-science","datacleaning","dataengineering","datamanipulation","machine-learning","pandas","python","scikit-learn","streamlit"],"latest_commit_sha":null,"homepage":"https://data-transformer.streamlit.app/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Rishav-Raj-Sinha.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-13T08:35:36.000Z","updated_at":"2024-09-15T16:05:01.000Z","dependencies_parsed_at":"2024-09-15T16:59:22.783Z","dependency_job_id":null,"html_url":"https://github.com/Rishav-Raj-Sinha/data_transformer","commit_stats":null,"previous_names":["rishav-raj-sinha/data_transformer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishav-Raj-Sinha%2Fdata_transformer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishav-Raj-Sinha%2Fdata_transformer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishav-Raj-Sinha%2Fdata_transformer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rishav-Raj-Sinha%2Fdata_transformer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Rishav-Raj-Sinha","download_url":"https://codeload.github.com/Rishav-Raj-Sinha/data_transformer/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243779079,"owners_count":20346656,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","datacleaning","dataengineering","datamanipulation","machine-learning","pandas","python","scikit-learn","streamlit"],"created_at":"2024-09-25T00:06:28.121Z","updated_at":"2026-01-02T09:20:40.561Z","avatar_url":"https://github.com/Rishav-Raj-Sinha.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Streamlit Data Preprocessing Application\n\n## Overview\n\nThis Streamlit application provides an interactive interface for data preprocessing tasks including:\n- Importing data\n- Handling missing values\n- Detecting and removing outliers\n- Feature selection/removal\n- Encoding categorical variables\n\nThe application is designed to assist users in cleaning and preparing datasets for analysis or machine learning.\n\n## Features\n\n1. **Data Import**: Upload CSV files and view dataset dimensions and content.\n2. **Handling Null Values**: Various methods to handle missing values including:\n   - Dropping rows or columns\n   - Replacing missing values with mean/median for numeric columns and the most frequent value for categorical columns\n3. **Handling Outliers**: Methods to detect and remove outliers using:\n   - Interquartile Range (IQR)\n   - Z-Score\n4. **Feature Selection/Removal**: Option to select and remove irrelevant columns.\n5. **Encoding**: Convert categorical values using:\n   - Label Encoding\n   - One-Hot Encoding\n\n## Installation\n\n### Prerequisites\n\nEnsure you have Python installed. You can download it from [python.org](https://www.python.org/downloads/).\n\n### Required Packages\n\nYou will need the following Python packages:\n- `streamlit`\n- `scikit-learn`\n- `pandas`\n- `numpy`\n- `matplotlib`\n\nYou can install these packages using pip:\n```bash\npip install streamlit scikit-learn pandas numpy matplotlib\n```\n\n## Usage\n\n1. **Run the Application**:\n\n   Navigate to the directory where your `app.py` file is located and run:\n   ```bash\n   streamlit run app.py\n   ```\n\n2. **Upload Data**:\n\n   Go to the \"Data Import\" tab and use the file uploader to upload your CSV file.\n\n3. **Handle Missing Values**:\n\n   In the \"Handling Null Values\" tab, choose a method to handle missing values and view the transformed dataset.\n\n4. **Detect and Remove Outliers**:\n\n   In the \"Handling Outliers\" tab, select an outlier detection method (IQR or Z-Score) to identify and optionally remove outliers.\n\n5. **Feature Selection/Removal**:\n\n   In the \"Feature Selection/Removal\" tab, choose columns to remove from the dataset and view the updated dataset.\n\n6. **Encode Categorical Variables**:\n\n   In the \"Encoding\" tab, choose an encoding method (Label Encoding or One-Hot Encoding) and see the transformed dataset.\n\n## Code Explanation\n\n### Handling Missing Values\n\n- **Drop Rows**: Removes rows with any missing values.\n- **Drop Columns**: Removes columns with any missing values.\n- **Replace Numeric Values with Mean**: Replaces missing numeric values with the column mean and non-numeric values with the most frequent value.\n- **Replace Numeric Values with Median**: Replaces missing numeric values with the column median and non-numeric values with the most frequent value.\n\n### Handling Outliers\n\n- **IQR Method**:\n  - Computes quartiles (Q1, Q3) and IQR.\n  - Identifies outliers as values below `Q1 - 1.5 * IQR` or above `Q3 + 1.5 * IQR`.\n  - Removes outliers based on these bounds.\n\n- **Z-Score Method**:\n  - Computes the mean and standard deviation of each numeric column.\n  - Calculates Z-Scores for each value.\n  - Identifies outliers as values with Z-Scores beyond a specified threshold (e.g., ±3).\n  - Removes outliers based on these Z-Scores.\n\n### Encoding\n\n- **Label Encoding**: Converts categorical values to integers.\n- **One-Hot Encoding**: Converts categorical values into binary columns, with each column representing a unique category.\n\n## Example\n\nHere’s a quick example of how to use the application:\n\n1. Upload your dataset.\n2. Handle any missing values by choosing an appropriate method.\n3. Detect and handle outliers using either the IQR or Z-Score method.\n4. Select and remove any irrelevant features.\n5. Encode categorical variables as needed.\n\n## Contributing\n\nFeel free to contribute by submitting issues or pull requests. For major changes, please open an issue to discuss what you would like to change.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n\n### Key Points\n\n- The README.md provides a comprehensive overview of the Streamlit application, including installation instructions, usage details, and explanations of the main features.\n- It includes code snippets and examples to help users understand how to interact with the application.\n- It outlines the methods used for handling missing values, detecting and removing outliers, and encoding categorical variables.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishav-raj-sinha%2Fdata_transformer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frishav-raj-sinha%2Fdata_transformer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frishav-raj-sinha%2Fdata_transformer/lists"}