In this project, I compare three different models for classification problems. The models that I used are XGBoost, Logistic Regression, and Random Forest Classifier. From this project, I can conclude that for this dataset, which has a small amount of data and features, Logistic Regression is the most optimal model. I think this project is quite fun, not only did I get to know more about the models and do all the tuning, but also I know the weakness and strengths of the models.
- Source : Loan or No Loan
- Training dataset : Training Dataset
- Testing dataset : Testing Dataset
There are 491 training data and 123 testing data in this dataset. In the training dataset, there are 12 independent columns and 1 dependent column.
- The first model that I used is XGBoost Classifier. The f1-score for this model is around 88 %.
- The second model that I used is Logistic Regression. The f1-score for this model is around 92 %.
- The last model that i used is Random Forest Classifier. The f1-score for this model is around 90 %.
After evaluating all the models, the models that optimal for this problem is Logistic Regression. The conclusion, this caused by lack of data that given in the dataset for training.
You can access the notebook in here