{"id":23387032,"url":"https://github.com/3bdalrhmans3d/dataqualityproject","last_synced_at":"2026-02-23T01:01:36.184Z","repository":{"id":269166211,"uuid":"903871708","full_name":"3bdalrhmanS3d/DataQualityProject","owner":"3bdalrhmanS3d","description":"An interactive web application for data quality analysis, machine learning, and conversational AI, built with Streamlit.","archived":false,"fork":false,"pushed_at":"2024-12-25T19:50:41.000Z","size":164430,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T12:47:18.518Z","etag":null,"topics":["data-analysis","data-visualization","ml","numpy","ollama","pandas","python","seaborn","streamlit"],"latest_commit_sha":null,"homepage":"","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/3bdalrhmanS3d.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":"2024-12-15T19:12:16.000Z","updated_at":"2025-04-06T19:12:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"5f9f4295-f609-4b1b-ac23-bb79029d44a5","html_url":"https://github.com/3bdalrhmanS3d/DataQualityProject","commit_stats":null,"previous_names":["3bdalrhmans3d/dataqualityproject"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/3bdalrhmanS3d/DataQualityProject","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3bdalrhmanS3d%2FDataQualityProject","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3bdalrhmanS3d%2FDataQualityProject/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3bdalrhmanS3d%2FDataQualityProject/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3bdalrhmanS3d%2FDataQualityProject/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/3bdalrhmanS3d","download_url":"https://codeload.github.com/3bdalrhmanS3d/DataQualityProject/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/3bdalrhmanS3d%2FDataQualityProject/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29733986,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-22T20:09:16.275Z","status":"ssl_error","status_checked_at":"2026-02-22T20:09:13.750Z","response_time":110,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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-analysis","data-visualization","ml","numpy","ollama","pandas","python","seaborn","streamlit"],"created_at":"2024-12-22T01:14:16.939Z","updated_at":"2026-02-23T01:01:36.080Z","avatar_url":"https://github.com/3bdalrhmanS3d.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Quality App\n\nThis is a Python-based web application built using **Streamlit** for performing common data quality tasks such as handling missing values, duplicates, and outliers in datasets. The app also integrates with **Ollama** for a chatbot interface to interact with the dataset and answer questions using a **Retrieval-Augmented Generation (RAG)** model.\n\nFor those who wish to try the app, you can access it [here](https://dataqualityproject.streamlit.app/).\n\n## Demo Video\n\nWatch the [demo](https://drive.google.com/file/d/1XfPMpp-l4iARA2FLsFLykLxrSUOTCEgC/view?usp=sharing)\n\n## Features\n\n### 1. Data Quality Analysis\n\n- **Dataset Upload:** Upload CSV or Excel files.\n- **Dataset Info:** View detailed dataset information including memory usage and data types.\n- **Describe Dataset:** Get descriptive statistics of the dataset.\n- **Handle Missing Values:** Fill or drop missing values with multiple options.\n- **Handle Duplicates:** Identify and remove duplicate rows.\n- **Outlier Detection:** Identify and handle outliers using various techniques.\n- **Data Type Conversion:** Convert data types, normalize, and transform columns.\n\n### 2. Data Visualization\n\n- **Interactive Plots:** Bar plots, pie charts, histograms, box plots, scatter plots, line charts, area charts, and pair plots.\n- **Correlation Matrices:** View correlation between features with heatmaps.\n- **Distribution Analysis:** Analyze data distributions using density and box plots.\n- **Custom Color Palettes:** Choose from various color palettes for visualizations.\n\n### 3. Machine Learning\n\n- **Model Comparison:** Compare multiple models (Random Forest, SVM, Logistic Regression).\n- **Feature Importance:** Analyze feature importance using RandomForestClassifier.\n- **Cross-Validation:** Perform cross-validation to evaluate model performance.\n- **Model Performance Metrics:** View accuracy, F1 score, precision, and recall.\n- **Interactive Prediction Interface:** Make predictions on new data.\n\n### 4. RAG-powered Chat\n\n- **Dataset Querying:** Query the dataset using natural language.\n- **Context-Aware Responses:** Get context-aware responses from the dataset.\n- **Code Snippet Generation:** Generate code snippets for data analysis.\n- **Interactive Chat Interface:** Chat with the dataset using Ollama's RAG model.\n\n## Prerequisites\n\nBefore running the project, make sure you have Python 3.12 installed on your system and Ollama (for RAG features).\n\n## Installation\n\n1. **Clone the repository (optional)**\n\n   ```bash\n   git clone https://github.com/3bdalrhmanS3d/DataQualityProject.git\n   cd DataQualityProject\n   ```\n\n2. **Create a virtual environment**\n\n   ```bash\n   python -m venv venv\n   ```\n\n3. **Activate the virtual environment**\n\n   On Windows:\n\n   ```bash\n   venv\\Scripts\\activate\n   ```\n\n   On macOS/Linux:\n\n   ```bash\n   source venv/bin/activate\n   ```\n\n4. **Install the required dependencies**\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n   Alternatively, install the required libraries manually:\n\n   ```bash\n   pip install streamlit pandas ollama scikit-learn matplotlib seaborn missingno imbalanced-learn\n   ```\n\n5. **Verify the installed libraries**\n\n   ```bash\n   pip list\n   ```\n\n6. **Run the Streamlit app**\n\n   ```bash\n   streamlit run RAG.py\n   ```\n\n   The app will open in your default web browser.\n\n## Project Structure\n\n```txt\n  DataQualityProject/\n  ├── RAG.py                 # Main application\n  ├── HandlingSection.py     # Data handling components\n  ├── PredictionManager.py   # ML model management\n  ├── requirements.txt       # Dependencies\n  └── README.md              # Documentation\n```\n\n## Usage\n\n- Upload your dataset (CSV or Excel) via the sidebar.\n- Select the task you want to perform from the navigation menu in the sidebar:\n  - **Dataset Info**: View detailed information about your dataset (columns, types, non-null counts).\n  - **Describe Dataset**: View the descriptive statistics of the dataset.\n  - **Handle Missing Values**: Choose to fill or drop missing values from columns.\n  - **Handle Duplicates**: Identify and remove duplicate rows.\n  - **Handle Outliers**: Remove outliers using the IQR method.\n  - **Chat using RAG**: Interact with your dataset via a chatbot powered by Ollama.\n\n## Download Modified Dataset\n\nAfter performing any changes, you can download the modified dataset by clicking the download button on the sidebar.\n\n## Requirements\n\n- **Python 3.12**\n- **Streamlit**: For creating the web interface.\n- **Pandas**: For data manipulation and analysis.\n- **Ollama**: For chatbot integration using the RAG model.\n\n---\n\n## Data Processing Features\n\n- **Missing Values:** Multiple imputation methods and visualizations.\n- **Outliers:** IQR-based detection and handling with visual analysis.\n- **Transformations:** Scaling, encoding, and normalization.\n- **Feature Engineering:** Automated and manual feature engineering options.\n\n## Machine Learning Capabilities\n\n- **Models:**\n  - Random Forest\n  - Support Vector Machines\n  - Logistic Regression\n- **Metrics:**\n  - Accuracy\n  - F1 Score\n  - Precision\n  - Recall\n- **Visualization:**\n  - Confusion Matrix\n  - ROC Curves\n  - Feature Importance\n\n## requirements.txt\n\n```txt\nstreamlit\npandas\nnumpy\nscikit-learn\nmatplotlib\nseaborn\nollama\nmissingno\nimbalanced-learn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F3bdalrhmans3d%2Fdataqualityproject","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F3bdalrhmans3d%2Fdataqualityproject","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F3bdalrhmans3d%2Fdataqualityproject/lists"}