Data set: The Abalone Data Set (Source: https://archive.ics.uci.edu/ml/datasets/abalone) Data set information These data consisted of 4,177 observations of 9 attributes, detailed as follows. Name / Da

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Data set: The Abalone Data Set

(Source: https://archive.ics.uci.edu/ml/datasets/abalone)

Data set information

These data consisted of 4,177 observations of 9 attributes, detailed as follows.

Name / Data Type / Measurement Unit / Description

—————————–

Sex / nominal / — / M, F, and I (infant)

Length / continuous / mm / Longest shell measurement

Diameter / continuous / mm / perpendicular to length

Height / continuous / mm / with meat in shell

Whole weight / continuous / grams / whole abalone

Shucked weight / continuous / grams / weight of meat

Viscera weight / continuous / grams / gut weight (after bleeding)

Shell weight / continuous / grams / after being dried

Rings / integer / — / +1.5 gives the age in years

Objective

Implement a Naïve Bayesian classifier to predict the age of abalone in Python from scratch.

Task requirements

(1) Randomly separate the data into two subsets: ~70% for training and ~30% for test.

(2) The Naïve Bayesian classifier must implements techniques to overcome the numerical underflows and zero counts.

(3) No ML library can be used in this task. The implementation must be developed from scratch. However, scientific computing libraries such as NumPy and SciPy are allow

Data set: The Abalone Data Set (Source: https://archive.ics.uci.edu/ml/datasets/abalone) Data set information These data consisted of 4,177 observations of 9 attributes, detailed as follows. Name / Da
CSCI316 (SIM) 202 2 Session 1 – Individual Assignment 1 CSCI316 – Big Data Mining Techniques and Implementation Individual Assignment 1 202 2 Session 1 (SIM) 15 Marks Deadline : Refer to the submission link of this assignment on Moodle Two (2) tasks are included in this assignment. The specification of each task starts in a separate page. You must implement and run all your Python code in Jupyter Notebook. The deliverables include one Jupyter Notebook source file (with .ipybn extension) and one PDF document for each task. Note: To generate a PDF file for a notebook source file, you can either (i) use the Web browser’s PDF printing function, or (ii) click “File” on top of the notebook, choose “Download as” and then “PDF via LaTex”. All results of your implementation must be reproducible from your submitted Jupyter notebook source files. In addition, the submission must include all execution outputs as well as clear explanation of your implementation algorithms (e.g., in the Markdown form at or as comments in your Python codes). Submission must be done online by using the submission link associated with assignment 1 for this subject on MOODLE. The size limit for all submitted materials is 20MB. DO NOT submit a zip file. Submissions made a fter the due time will be assessed as late submissions. Late submissions are counted in full day increments (i.e. 1 minute late counts as a 1 day late submission). There is a 25% penalty for each day after the due date including weekends. The submission s ite closes four days after the due date. No submission will be accepted after the submission site has closed. This is an individual assignment . Plagiarism of any part of the assignment will result in having 0 mark for the assignment and for all students involved. Marking guidelines Your Python code will be assessed. The computers in the lab define the standard environment for code development and code execution. Note that the correctness, completeness, efficiency, and results of your executed code will be assessed. Thus, code that produces no useful outputs will receive zero marks. This also means that code that does not run on a computer in the lab would be awarded zero marks or code where none of the core functions produce correct results wou ld be awarded zero marks. The clearness of explanations for your answers will be assessed. CSCI316 (SIM) 202 2 Session 1 – Individual Assignment 1 Task 1 (6 marks) Dataset : Risk Factor prediction of Chronic Kidney Disease Data set Source: https://archive.ics.uci.edu/ml/datasets/Risk+Factor+prediction+of+Chronic+Kidney+Disease Objective Use Pandas in Python to clean and pre -pro cess the raw data in this dataset . Requirements (1) Load the row data into Pandas with the meta information being removed . (2) Apply ordinal encoding to all columns contains numerical bins . For instance, in the “age” column, 0 for “<12”, 1 for “12 -20”, 2 for “20 -27”, etc. (3) Apply one -hot -encoding to the “stage” column . (4) Find out and rank the correlation s between the “class” column and all other columns Deliverables • A Jupiter Notebook source file named _task1.ipybn which contains your implementation source code in Python • A PDF document named _task1.pdf which is generated from your Jupiter Notebook source file , and presents clear and accurate explanation of your implementation and results. CSCI316 (SIM) 202 2 Session 1 – Individual Assignment 1 Task 2 (9 marks) Dataset : The Secondary Mushroom Dataset Source : https://archive.ics.uci.edu/ml/datasets/Secondary+Mushroom+Dataset This dataset includes 61 ,069 hypothetical mushroom . Each mushroom is identified as edible, poisonous, or of unknown edibility and not recommended (the latter class was combined with the po isonous class). Objective The objective of this task is to implement from scratch Decision Tree classification method to predict whether the mushroom samples are poisonous (p) or editable (e) (t hus a binary classification problem ). The data are included in the file named “ secondary_data.csv ” (not “ primary_data.csv ”!), and the meta information is in the file named “ secondary_data _meta .csv ”. Before training the models, you first need to pre -process the data ( e.g. ., separate the values). Missing va lues are included in the data. Requirements (1) Implement two DT models by using any two split criteria from I nformation Gain, Gain Ratio, Gini Index and Variance. Note that you can use either binary -split or multiple -split. (2) Use ~60% samples for training , ~20% for post -pruning , and ~20% for testing. (3) Report the accuracy of the model s. (4) All DT models must be self -implemented , that is, y ou cannot use any machine learning library in this task. (5) It is recommended that your implementation includes a “tree induction function ”, a “classification function ” and a “post -pruning function ”. (6) You can (but not must) use any suitable pre -processing method. You a lso can (but not must) use any reasonable early stopping criteria (pre -pruned parameters such as number of splits, minimum data set size, and split threshold ) to improve the training speed . If you do so, explain your reasons. (7) Present clear and accurate ex planation of your implementation and results (in the Markdown format). Deliverables • A Jupiter Notebook source file named _task 2.ipybn which contains your implementation source code in Python • A PDF document named _task 2.pdf which is generated from your Jupiter Notebook source file . • Present clear and accurate explanation of your implementation methods and results (i.e., in the Markdown format) .

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