# can you help me to solve this assignment. this is all about coding. pls let me k

can you help me to solve this assignment. this is all about coding. pls let me know ASAP

Document Preview:

ICT707 Big Data Assignment

Big Data Assignment Marking Criteria

The Big Data Assignment is comprised of two parts:

The first part is to create the algorithms in the tasks, namely: Decision Tree, Gradient Boosted Tree and Linear regression and then to apply them to the bike sharing dataset provided. Try and produce the output given in the task sections (also given in the Big-Data Assignment.docx provided on Blackboard).

The second part is then use those algorithms created in the first part and apply them to another dataset chosen from Kaggle (other than the bike sharing dataset provided).

Rubric

Datasetsbike sharing [provided]Student selected dataset [from Kaggle.com]Decision Tree

Decision Tree55Decision Tree Categorical features55Decision Tree Log55Decision Tree Max Bins55Decision Tree Max Depth55Gradient Boosted TreeGradient Boosted Tree55Gradient boost tree iterations55Gradient boost tree Max Bins55Linear regressionLinear regression55Linear regression Cross Validation

Intercept55Iterations55Step size55L1 Regularization55L2 Regularization55Linear regression Log557575Total mark 150What needs to be submitted for marking:

For the Decision tree section a .py or .ipynb file for each of the following:

Decision Tree

Decision Tree Categorical features

Decision Tree Log

Decision Tree Max Bins

Decision Tree Max Depth

For the Gradient boost tree section a .py or .ipynb file for each of the following:

Gradient boost tree

Gradient boost tree iterations

Gradient boost tree Max Bins

For the Linear regression section a .py or .ipynb file for each of the following:

Linear regression

Linear regression Cross Validation

Intercept

Iterations

Step size

L1 Regularization

L2 Regularization

Linear regression Log

Each of the files submitted will be tested with the following datasets:

bike sharing [which is provided on blackboard]
A dataset of the students choice downloaded from…