A Digital Marketing Conversion Rate Prediction system that uses ML to predict campaign performance, logs metrics in CSV, and visualizes insights using Power BI.
The Digital Marketing Campaign Conversion Rate project is a Machine Learning-based analytics system designed to predict the effectiveness of marketing campaigns. The system uses historical campaign data such as ad spend, channel, target audience, engagement metrics, and previous conversions as input features.
A supervised ML model (e.g., Logistic Regression, Random Forest, or XGBoost) is trained to predict the probability of conversions for future campaigns. Model performance metrics such as accuracy, precision, recall, and F1-score are recorded and exported to a CSV file for tracking and analysis.
Power BI is used for visualizing campaign performance, highlighting key trends, conversion rates, and feature importance in an interactive dashboard. This allows marketing teams to make data-driven decisions, optimize ad spend, and improve ROI.
This project demonstrates practical implementation of ML modeling, model evaluation, CSV reporting, and business intelligence visualization, making it a strong major project for AI, ML, and Digital Marketing analytics.
Well-documented and organized code with comments
SQL files with sample data and schema
Detailed project report & documentation
30 days of free technical support