Decision Trees

Decision Tree is a great basic classification algorithm. It uses a tree based approach of "if-else" like branches to decide upon the category or class of a test data. Here is a to-do list I would like you to complete:

- [ ] Apply Decision Tree Regression using Python on this [Dataset](https://www.kaggle.com/dansbecker/melbourne-housing-snapshot).
- [ ] Check out the parameters that used to correctly fit the model. For help, check out this [Link](https://www.kaggle.com/dansbecker/underfitting-and-overfitting).
- [ ] Find out difference between Decision Tree Regression and Decision Tree Classification.
- [ ] Try applying Decision Tree Classification Algorithm on the given [Data set](https://www.kaggle.com/uciml/pima-indians-diabetes-database).

Start by reviewing the repository's existing guide structure and notebook organization, then inspect the Melbourne housing and Pima Indians Diabetes datasets linked in the issue. Work through decision tree regression, parameter fitting, the regression/classification distinction, and classification, with completed notebooks or documented results showing each requested item.

Written by the indexing model from the issue text.