Bank Churn Prediction (Algo: Gradient Boost)

About this algo:

- I created this model with a 'Gradient Boost' algo on a dataset of 10,000 customers in a bank with an accuracy of 87.45%
- I tested this using models like Logistic Regression Classifier (accuracy: 82.90%), Decision Tree Classifier (accuracy: 80.00%), Random Forest (accuracy score: 87.00%), K Nearest Neighbour (accuracy score: 85.55%), Support Vector machines (accuracy: 86.15%), Gradient Boost Classifier (accuracy: 87.45%).

Use cases of the underlying principle:

- The Gradient Boost algorithm applies a new model (say decision Trees) each time and corrects the errors of the previous one by focusing on the data points with the highest errors. Using decision trees it creates a strong predictive model through optimization of a loss function.(A loss function measures how far a model's predictions are from the actual values)
- This relatively complex model can be used when linear relationships are not clearly visible and decision trees are struggling to give pure direct answers. We thus use a learning rate to fix the accuracy.

Dataset: What does the underlying data look like?:

- The dataset was an excel sheet of values of the same parameters you see above in the from.
- Data Training: 80% of that data was used to train the model and 20% was used to test it which gave you the accuracy score you see above.
model and 20% was used to test it which gave you the accuracy score you see above.