Portfolio

Enhanced Data Quality Platform

Title

LLM-Powered Comprehensive Data Quality Assessment and Advanced Analytics Platform

Overview

The Enhanced Data Quality Platform is an advanced, AI-powered solution designed to automate and streamline the process of data quality assessment, analysis, and improvement across diverse datasets. This platform provides a comprehensive approach to enhancing data quality, offering features such as model fine-tuning, data analysis, metadata enrichment, and actionable insights, all while being flexible and adaptable to various datasets and use cases.

Objective

Develop a state-of-the-art, scalable platform that leverages large language models (LLMs), traditional statistical methods, and advanced machine learning techniques to provide comprehensive data quality assessment, metadata enhancement, intelligent analytics, and predictive capabilities across diverse datasets.

Problem Statement

Traditional data quality tools often lack the contextual understanding, flexibility, and advanced analytical capabilities needed to handle diverse and complex datasets effectively. This project aims to bridge this gap by creating an advanced platform that combines statistical analysis, machine learning techniques, and LLM-powered insights to provide a more nuanced, adaptable, and comprehensive approach to data quality management, analysis, and predictive modeling.

Features

1. Flexible Data Handling

• Supports multiple data formats including CSV, JSON, JSON.gz, Excel, SQLite, Parquet, and SQL databases.
• Allows for easy addition, deletion, and modification of data sources.

2. Model Integration and Fine-Tuning

• Integrates with Large Language Models (LLMs) and allows easy switching between models.
• Automatically fine-tunes models using provided datasets for specific use cases.

3. Data Quality Assessment

• Basic Quality Checks: Detects missing values, duplicates, and assesses data types.
• Advanced Quality Checks: Uses Great Expectations to validate data against predefined expectations.
• LLM Quality Assessment: Uses natural language processing to provide detailed insights into data quality.

4. Metadata Generation

• Generates metadata, including column types, unique values, memory usage, and schema.
• Enriches metadata using LLMs for a better understanding of datasets.

5. Data Quality Improvement Recommendations

• Generates actionable recommendations for improving data quality, covering data cleaning, transformation, and ethical considerations.

6. Comprehensive Data Analysis Capabilities

• Statistical Analysis: Calculates key statistical metrics to understand data distributions.
• Time Series Analysis: Analyzes temporal patterns including trends and seasonality.
• Causal Inference: Determines the effect of treatments or interventions on outcomes.

7. Data Visualization

• Generates data quality dashboards to visualize data patterns and relationships using Plotly, Matplotlib, and Seaborn.

8. Feature Selection and Dimensionality Reduction

• Feature Selection: Uses methods like mutual information, ANOVA, and random forests.
• Dimensionality Reduction: Applies PCA to simplify datasets.

9. Clustering and Correlation Analysis

• Clustering: Uses methods like K-Means to identify natural groupings within data.
• Correlation Analysis: Analyzes and visualizes correlations between features.

10. Anomaly Detection and Data Drift Analysis

• Detects anomalies using Isolation Forest and Local Outlier Factor.
• Analyzes data drift to understand changes in data distribution over time.

11. Text and Network Analysis

• Text Analysis: Uses NLP techniques like TF-IDF and LDA for topic discovery.
• Network Analysis: Constructs network graphs to understand relationships in data.

12. Integration with Existing Workflows

• Seamlessly integrates with existing data pipelines and workflows.

13. Synthetic Data Generation

• Generates synthetic data that resembles original datasets while maintaining statistical properties.

14. Reporting and Documentation

• Generates comprehensive reports including data quality assessments, metadata, recommendations, and more.
• Saves reports in structured formats (e.g., JSON) for easy sharing.

15. Semantic Search and Vector Stores

• Builds vector stores using FAISS for efficient semantic search.
• Supports Retrieval-Augmented Generation (RAG) for answering queries based on retrieved documents.

16. User-Friendly Interface and Logging

• Provides a command-line interface with clear logging for task progress and troubleshooting.

Supported Datasets

The platform is designed to work with a variety of datasets, including but not limited to:

1. Customer Data (CSV, JSON, Parquet formats)
2. Product Reviews (JSON, JSON.gz formats)
3. Time Series Data (e.g., sales data with timestamps)
4. Categorical and Numerical Data for hypothesis testing
5. SQL databases
6. Amazon Reviews Dataset (JSON)
7. Alpaca Dataset (JSON)
8. Online Retail Dataset (Excel)
9. User-defined datasets in CSV, JSON, Excel, SQLite, or Parquet formats

Project Scope

• Flexible data loading from various sources (CSV, JSON, JSON.gz, SQL databases, Excel, Parquet)
• Comprehensive data quality assessment combining statistical, machine learning, and LLM-based methods
• Advanced metadata generation and enhancement using LLMs
• Intelligent recommendations for data improvement and analysis
• Semantic search capabilities using vector stores
• Data drift analysis and anomaly detection
• Synthetic data generation for testing and augmentation
• Feature selection and dimensionality reduction
• Clustering analysis and correlation studies
• Time series analysis and decomposition
• Hypothesis testing for statistical inference
• Model fine-tuning capabilities for specific tasks
• Comprehensive reporting and interactive visualization of results
• Parallel processing for improved performance
• Integration with Great Expectations for additional data validation

Methodology and Models

The platform utilizes a combination of traditional statistical methods, machine learning techniques, and advanced language models:

1. Statistical Analysis:

   • Pandas and NumPy for data manipulation and basic statistics
   • SciPy for advanced statistical tests
   • Statsmodels for time series analysis and statistical modeling

2. Machine Learning:

   • Scikit-learn for feature selection, clustering, anomaly detection, and predictive modeling
   • Isolation Forest and Local Outlier Factor for anomaly detection

3. Deep Learning and LLMs:

   • PyTorch and Transformers library for working with pre-trained models
   • Integration with models like T5, BART, or custom fine-tuned models

4. Natural Language Processing:

   • LangChain for LLM integration and chain-of-thought prompting
   • Sentence transformers for text embeddings

5. Vector Storage and Search:

   • FAISS for efficient similarity search

6. Visualization:

   • Matplotlib and Seaborn for static visualizations
   • Plotly for interactive dashboards

7. Data Validation:

   • Great Expectations for additional data validation and quality checks

Approach

1. Data Ingestion: Implement flexible data loading from various sources
2. Quality Assessment: Combine statistical checks, machine learning techniques, and LLM-powered analysis
3. Metadata Enhancement: Use LLMs to generate rich, context-aware metadata
4. Recommendation Generation: Provide intelligent suggestions for data improvement and analysis
5. Search and Retrieval: Implement semantic search using vector stores and LLMs
6. Advanced Analytics: Perform clustering, dimensionality reduction, time series analysis, and hypothesis testing
7. Anomaly Detection: Implement multiple methods for identifying outliers and anomalies
8. Predictive Modeling: Fine-tune models for specific tasks when necessary
9. Reporting and Visualization: Generate comprehensive reports and interactive dashboards

File Structure

• main.py: Core implementation of the EnhancedDataQualityPlatform class
• model_evaluation.py: Script for evaluating and selecting the best LLM and embedding models
• test_platform.py: Comprehensive unit tests for the EnhancedDataQualityPlatform class
• main_dynamic.py: Dynamic main script that uses the best model configuration from evaluation

Installation and Usage

1. Clone the Repository:

2. Install Dependencies: Ensure you have Python 3.8 or above. Install the necessary packages:

3. Run the Platform: Start by running the main Python script:

4. Dataset Configuration: Modify dataset_config.json to specify the datasets you wish to analyze.