Decision Tree Project

What have we learnt so far

We have seen how to clean the data and how to select features and learnt how to apply the following:

-- By the completing this Assignment --

you will get hands-on practice on how decision tree is performing for both classification and Regression and how it is different from the Linear regression and Logistic Regression
Implementation of Grid search cv and Randomized search Cv
You will get to learn how hyper parameter tuning helps in model performance

** For Decision tree Regressor**

we are using the same dataset of Housing prices, we had used for Linear Regression

** For Decision tree Classifier**

we are using the same dataset of Loan Prediction, we had used it earlier in Logistic Regression

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
.idea		.idea
.ipynb_checkpoints		.ipynb_checkpoints
data		data
images		images
q01_my_decision_regressor		q01_my_decision_regressor
q02_decision_regressor_plot		q02_decision_regressor_plot
q03_my_decision_classifier		q03_my_decision_classifier
q04_decision_classifier_plot		q04_decision_classifier_plot
README.md		README.md
__init__.py		__init__.py
__init__.pyc		__init__.pyc
config.json		config.json
q01_my_decision_regressor.ipynb		q01_my_decision_regressor.ipynb
q02_decision_regressor_plot.ipynb		q02_decision_regressor_plot.ipynb
q03_my_decision_classifier.ipynb		q03_my_decision_classifier.ipynb
q04_decision_classifier_plot.ipynb		q04_decision_classifier_plot.ipynb