Data quality is the backbone of reliable analysis and machine learning. The Automated Data Cleaning Framework is designed to detect, clean, and validate messy datasets efficiently. Using the Yelp dataset as a case study, this project demonstrates how automation can save time, improve accuracy, and deliver analysis-ready data.
Technologies Stack
- Python (Pandas, NumPy, Matplotlib)
- Jupyter Notebooks for analysis
- GitHub for collaboration and version control
Why it Matters
Messy data can lead to unreliable insights and wasted resources. Common problems include:
- Missing values
- Duplicate records
- Inconsistent formats
- Poorly structured categories
This project provides an automated and adaptable solution to handle these challenges.
Project Objectives
The main goals of this project are:
- Exploratory Data Analysis (EDA): Automatically understand dataset structure and quality.
- Data Cleaning: Handle missing values, remove duplicates, and standardize formats.
- Adaptability: Create a modular framework that adjusts easily to new datasets.
- Validation: Ensure cleaned data meets quality standards for analysis and machine learning.
Key Features of the Framework
- Exploratory Data Analysis – Detects missing values, outliers, and inconsistencies.
- Automated Cleaning Functions – Handles nulls, duplicates, and inconsistent entries.
- Modular & Adaptable – Works with different datasets beyond Yelp.
- Validation Module – Ensures data is clean, consistent, and analysis-ready.
Workflow / Architecture
Pipeline Overview:
Raw Data → Exploratory Analysis → Cleaning Module → Validation → Ready-to-Use Data
This pipeline ensures a smooth transition from raw, unstructured data to a clean and reliable dataset.
Case Study – Yelp Dataset
We applied the framework to the Yelp dataset (150,000+ business records).
Before Cleaning:
- 13k+ missing values in attributes
- 23k+ missing values in business hours
- Inconsistent category tags
After Cleaning:
- Standardized categories
- Reduced missing values significantly
- Structured and validated dataset ready for analytics and ML
Results & Benefits
✔ Saves hours of manual preprocessing
✔ Produces consistent and trustworthy datasets
✔ Adaptable to any new dataset structure
✔ Improves quality of downstream analysis and ML models
