Machine Learning Model to Predict CTR with Python

Machine-Learning-Model-to-Predict-CTR-with-Python

This project is aimed at building machine learning models to predict click-through rates (CTR) based on historical marketing data. The model performances are measured using statistical metrics such as MSE, RMSE, MAE and R-squared.

The problem was approached from a regression standpoint due to the CTR feature being a continuous variable. The machine learning algorithms implemented include:

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor
• XGBoost Regressor

The impact of the model features on prediction values was analysed using SHAP values and visualisation (beeswarm, waterfall)

View the full project on GitHub - https://github.com/AdemolaI/Machine-Learning-Model-to-Predict-CTR-with-Python

Data Information.

Source: https://www.kaggle.com/datasets/rahulchavan99/marketing-campaign-dataset





SHAP beeswarm plot of top ten features from XGBoost regressor model.