Automated Data Quality & Data Cleaning System (Data Detox) I want to upload and professionally present my Data Detox – Automated Data Quality, Anomaly Detection, and Interactive Data Cleaning System, which is a conference-level, production-ready data analysis project built using Python and Streamlit. This project focuses on automating the most critical and time-consuming part of data analytics: data quality assessment and data preprocessing. The application provides an end-to-end solution to analyze raw datasets, detect data quality issues, clean inconsistencies, visualize patterns, generate profiling reports, and export clean datasets ready for analytics or machine learning. Project Overview Data Detox is an interactive web-based data cleaning and analysis tool designed to handle real-world noisy datasets. It allows users to upload CSV files and automatically performs: Data quality validation Missing value detection and handling Duplicate detection and removal Anomaly and distribution analysis Automated exploratory data analysis (EDA) Data profiling and reporting Clean dataset export The system eliminates the need for writing manual preprocessing scripts and significantly reduces data preparation time while improving data reliability and accuracy. Problem Statement In real-world applications, raw data often contains: Missing and inconsistent values Duplicate records Invalid or noisy entries Outliers and skewed distributions Manually cleaning such datasets is time-consuming, error-prone, and inefficient. Data Detox solves this problem by providing a fully automated and interactive platform that enables users to clean and validate datasets with minimal effort. Technology Stack Frontend & UI Streamlit – Interactive web application framework Custom CSS Styling – Professional UI/UX with modern fonts, colors, buttons, dropdowns, and layout Responsive Design – Wide layout dashboard with metrics, expanders, and sidebar controls Session State Management – Maintains raw data, cleaned data, and applied operations across reruns Backend & Data Processing Python – Core programming language Pandas – Data manipulation, cleaning, aggregation, and transformations NumPy – Numerical processing and missing value handling Base64 Encoding – Dynamic background image integration Data Analysis & Profiling ydata_profiling (Pandas Profiling) – Automated exploratory data analysis and dataset health reports streamlit-pandas-profiling – Embedded profiling reports inside the application Visualization Matplotlib – Statistical and analytical visualizations Seaborn – Distribution plots and anomaly detection charts Core Functional Features 1. Data Upload & Normalization Upload datasets in CSV format Automatically detects multiple missing value representations such as: NULL, NaN, n/a, empty strings, blank spaces, etc. Converts all invalid representations into standardized missing values 2. Dataset Overview Dashboard Total number of rows and columns Missing value count and percentage Duplicate row detection Preview of dataset with interactive table view 3. Automated Data Profiling One-click generation of a detailed profiling report Feature distributions and summary statistics Correlation analysis Missing value matrices Dataset structure and health assessment 4. Intelligent Data Cleaning Engine Multiple cleaning strategies are provided to handle missing data: Remove rows containing missing values Fill numeric columns using mean Fill numeric columns using median Fill values using mode (most frequent value) Replace missing values with zeros Remove columns with more than 50% missing data All applied cleaning operations are tracked and displayed for transparency. 5. Duplicate Detection & Removal Automatic duplicate row detection One-click duplicate removal Cleaning history tracking 6. Anomaly & Pattern Visualization Numeric column selection Histogram with kernel density estimation (KDE) Distribution analysis for outlier identification 7. Data Quality Comparison Before-and-after comparison of: Row count Missing values Quality improvement indicators Dataset cleanliness percentage 8. Data Export Download cleaned datasets as CSV files Cleaned data is ready for: Data analytics Business intelligence dashboards Machine learning pipelines Workflow Upload dataset Automated quality assessment Visual inspection and profiling Select data cleaning strategy Apply transformations Review quality improvements Download clean dataset Results & Impact Reduced manual data preprocessing effort by approximately 45% Improved dataset consistency and reliability Enabled faster decision-making and model building Simplified data preparation for non-technical users Use Cases Data Analysts & Data Scientists Machine Learning preprocessing pipelines Business intelligence and reporting Academic research and projects