Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.
INTRODUCTION: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, held in conjunction with KDD-98, The Fourth International Conference on Knowledge Discovery and Data Mining. The modeling task is a binary classification problem where the goal is to estimate the likelihood of donation from a direct mailing campaign.
In this Take1 iteration, we will build and test models using the minimal set of basic features. The final model will serve as the baseline result as we employ more features in future iterations.
ANALYSIS: In this iteration, the average performance of the machine learning algorithms achieved a ROC/AUC benchmark of 70.99% using the training dataset. Furthermore, we selected Random Forest as the final model as it processed the training dataset with a final ROC/AUC score of 77.23%. When we processed the test dataset with the last model, the model achieved a ROC/AUC score of 50.42%.
CONCLUSION: In this iteration, the Random Forest model appeared to be suitable for modeling this dataset. However, we should explore the possibilities of using more features from the dataset to model this problem.
Dataset Used: KDD Cup 1998 Dataset
Dataset ML Model: Binary classification with numerical and categorical features
Dataset Reference: https://kdd.org/kdd-cup/view/kdd-cup-1998/Data
The HTML formatted report can be found here on GitHub.