Advanced EDA, Vectorized Pipelines, and High-Fidelity Feature Stores

Project Overview

A production-grade data preprocessing pipeline implementing the Input-Process-Output (IPO) architectural pattern. This project handles professional data cleaning, robust feature engineering, statistical outlier mitigation, and strict schema validation for e-commerce data.

Key Performance Indicators

Metric	Value	Business Value
Total Revenue Processed	$1,264,371.82	Scalable processing of large-scale financial inputs
Average Order Value	$1,053.64	Accurate tracking of transactional baselines
Outliers Neutralized	8	Distribution variance protected via IQR capping
Features Engineered	3	Manufactured indicators for customer segmentation
Data Integrity Pass Rate	100%	Guaranteed schema alignment via Pandera

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Pipeline Architecture (IPO Pattern)

The data flows through a strict three-tier modular architecture designed for production stability:

[INPUT LAYER]     ➔      [PROCESS LAYER]     ➔     [OUTPUT LAYER]
─────────────────         ─────────────────         ────────────────
• Handling Missing Data   • Vectorized Calculations • Pandera Contract Validation
• Removing Duplicates     • Outlier Capping (IQR)   • Integrity Verifications
• Type Assertions         • Feature Manufacturing   • Clean Asset Export (.csv)

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Core Technical Features

1. Automated Missing Data Matrix

• < 5% Missingness: Automatic row deletion.
• 5% - 20% Missingness: Skew-resistant Median Imputation.
• > 20% Missingness: Categorical Proxy Imputation (e.g., handling CouponCode gaps by mapping to "None" without losing transactional history).

2. High-Fidelity Feature Engineering

• OrderSizeCategory: Quantile-based customer segmentation (Low, Mid, High-Value).
• IsPremiumProduct: Binary indicator capturing high-margin orders against median pricing.
• IsDiscounted: Track performance metrics by isolating coupon usage.

3. Non-Parametric Outlier Mitigation

• Utilizes the Interquartile Range (IQR) method to isolate anomalies.
• Implements upper and lower fence boundary capping via NumPy to prevent distribution distortion without dropping valid data points.

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Quick Start

1. Installation & Setup

# Clone the repository
git clone [https://github.com/riazfatima/Advanced-EDA-and-Feature-Engineering.git]
cd Advanced-EDA-and-Feature-Engineering

# Install dependencies
pip install -r requirements.txt



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## 🎓 Core Competencies Demonstrated

1. **Production Architecture:** Decoupled stages ensuring high code maintainability.
2. **Defensive Data Design:** Data contract implementation using `pandera`.
3. **Mathematical Rigor:** Non-parametric statistical boundaries applied to messy business data.
4. **Vectorization:** High-efficiency processing leveraging native Pandas and NumPy methods.