Adludio Data Science Challenge

Overview

I developed a data analytics and prediction system for Adludio's digital advertising platform. This project combines modern data engineering with advanced machine learning to analyze ad campaign performance and predict success metrics. The system processes real campaign data to identify patterns and factors that influence engagement and conversion rates.

Key Features

• End-to-End ETL Pipeline: Built a robust data pipeline using Docker, PostgreSQL, Airflow, and DBT for efficient data processing and transformation
• Intelligent Ad Performance Analysis: Implemented comprehensive exploratory data analysis to extract actionable insights from complex ad campaign data
• Predictive Modeling: Developed machine learning models to predict engagement rates and click-through rates, enabling data-driven optimization of campaign strategies
• Interactive Data Visualization: Created interactive dashboards with Redash to visualize campaign metrics and performance insights
• Data Validation Framework: Implemented automated data validation to ensure data integrity throughout the pipeline

Technical Implementation

• Data Engineering: Constructed a containerized ETL architecture with data validation mechanisms
• Statistical Analysis: Applied advanced statistical methods to identify performance patterns and anomalies
• Machine Learning Pipeline: Built and evaluated multiple models including Random Forest and Logistic Regression for performance prediction
• Feature Engineering: Created domain-specific features to enhance prediction accuracy
• Model Deployment: Developed a production-ready model serving infrastructure

Bonus Features

• Computer Vision Integration: Implemented advanced computer vision techniques to analyze visual ad content:
  • Text detection using both Tesseract OCR and EAST text detector
  • Object detection for identifying key visual elements in advertisements
  • Color analysis to understand color psychology in ad performance