CS5228 Knowledge Discovery And Data Mining Assignment

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CS5228 Knowledge Discovery And Data Mining Assignment - National University of Singapore, Singapore

Introduction - In this assignment, you are going to implement a Decision Tree Regressor, a Gradient Boosting Regressor, and a Hierarchical Clustering Algorithm.

Decision Tree Regressor -

In this part, you are going to implement a Least Squares Regressor Tree. We have provided the data and the test cases to you. You should use Python 3.X in your implementation.

CART (Classification and Regression Trees), a non-parametric statistical algorithm, is developed by Breiman, Friedman, Olshen, Stone in early 80's. CART can be used to predict or analyze both categorical (classification) and continuous or numerical (regression) data.

Gradient Boosting Regressor -

In this part, you are going to implement a Gradient Boosting Regressor based on the Least Squares Regressor Tree you have implemented in part 2. We have provided the data and the test cases to you. You should use Python 3.X in your implementation.

Hierarchical Clustering Algorithm -

In this part, you are going to implement an Agglomerative Hierarchical Clustering Algorithm. We have provided the data and the test cases to you. You should use Python 3.X in your implementation.

You can use Numpy in your implementation. But DO NOT use external machine learning libraries like scipy, scikit-learn in your implementation. DO NOT copy the code from the internet, e.g. Github.

Attachment:- Knowledge Discovery And Data Mining Assignment Files.rar

Reference no: EM132383232

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