Digital Marketing Conversion Prediction

Welcome to our Digital Marketing Conversion Predcition Project!.

This project aims to enhance campaign effectiveness in the digital marketing sector by
accurately predicting customer conversions. By leveraging machine learning, the project
seeks to identify potential converters and optimize marketing strategies. The objective
is to develop a robust machine learning model that predicts customer conversions based on
various demographic and engagement factors, enabling improved campaign targeting,
increased conversion rates, and maximized return on advertising spend (ROAS).

Key Features of the Project:

Thorough analysis of Digital Marketing data CSV files.
Answering important questions to extract valuable insights.
Visualizing the data to uncover patterns and trends.
Providing meaningful interpretations and recommendations based on the analysis.
Implementing the necessary Machine Learning Algorithm for predicting the conversion

Why Digital Marketing Prediction Data so Important:

Information

Algorithms Used :

1. Logistic Regression :

Provided a simple and interpretable baseline model, offering a quick understanding of the relationships between features and the target variable.

2. Support Vector Machines :

Was effective in handling the high-dimensional feature space, particularly after hyperparameter tuning via GridSearchCV, and showed good performance on the dataset.

3. Decision Tree :

Offered easy interpretability but was prone to overfitting. However, it helped in understanding the data and visualizing feature importance.

4. Random Forest :

Improved upon the Decision Tree by reducing overfitting through ensemble learning, resulting in a more robust model with better generalization to unseen data.

5. XGBoost / Gradient Boosting :

Demonstrated strong performance with its gradient boosting framework, particularly excelling in handling complex patterns within the data and offering high accuracy after hyperparameter optimization.

6. Final Accuracy Table :