User Engagement Prediction for Digital Ads

This project implements and compares several machine learning models to predict whether a user will click on an advertisement (CTR prediction) using a provided dataset. The models include XGBoost (with hyperparameter tuning and bagging), Random Forest, Decision Tree, and Logistic Regression.

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Features

• Data Preprocessing: Handles categorical variables using label encoding and standardizes features where appropriate.
• Model Training: Trains and evaluates multiple models:
  • XGBoost (with GridSearchCV for hyperparameter tuning)
  • XGBoost with Bagging
  • Random Forest (with cross-validation)
  • Decision Tree
  • Logistic Regression
• Evaluation Metrics: Reports accuracy and classification metrics for each model.
• Comparison: Enables easy comparison of model performance for CTR prediction.

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Requirements

• Python 3.7+
• pandas
• numpy
• scikit-learn
• xgboost

Install dependencies with:

pip install pandas numpy scikit-learn xgboost

Model Details

• XGBoost: Uses grid search for hyperparameter optimization and 5-fold cross-validation.
• Bagging with XGBoost: Uses BaggingClassifier to ensemble XGBoost models.
• Random Forest: Evaluated with cross-validation and test set.
• Decision Tree & Logistic Regression: Standard training and evaluation.

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Results

After running the notebook, you will see:

• Accuracy for each model (on test set and via cross-validation where applicable)
• Classification reports (precision, recall, f1-score)
• Best hyperparameters for XGBoost (via GridSearchCV)

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