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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

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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…

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