Difference between revisions of "Fall 2024: Data Mining Lab"

Revision as of 06:09, 29 August 2024

Please be on time to avoid the Attendance Penalty.
Please sign on the Attendance Register before your take a seat.
Please put your mobile phone in the Silent Mode.
Each lab assignment needs to be submitted in the Google Classroom for evaluation(will be notified in the GC lab-wise, submit before the deadline).
Turn off(shut down) your assigned computer and arrange the chair before you leave the lab.

Q. NO.	Program	Practical No.	Remarks
1	https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial1/tutorial1.html	Practice Set No. 1	Introduction to Python
2	https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html	Practice Set No. 2	Introduction to Numpy and Pandas
3	https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial3/tutorial3.html	Practice Set No. 3	Data Exploration

Q. NO.	Program	Practical No.	Remarks
1	Apply data cleaning techniques on any dataset (e.g. Chronic Kidney Disease dataset from UCI repository). Techniques may include handling missing values, outliers and inconsistent values. Also, a set of validation rules may be specified for the particular dataset and validation checks performed.	Practical No. 1	Dataset: kidney_disease.csv Download from Kaggle: Chronic KIdney Disease dataset on Handling Missing values

Team No.	Project Title	Team Members	Outcomes/Remarks
1	Any Classification/Clustering Problem	1 2 3 4	Dataset: kidney_disease.csv Report: Project Presentation:
2	Any Classification/Clustering Problem	1 2 3 4	Dataset: kidney_disease.csv Report: Project Presentation:

@@ Line 43: / Line 43: @@
 Download from Kaggle:
 [https://www.kaggle.com/datasets/mansoordaku/ckdisease Chronic KIdney Disease dataset]
+[https://www.kaggle.com/code/alexisbcook/handling-missing-values#How-many-missing-data-points-do-we-have?|Tutorial on Handling Missing values]
 |}
 == '''Projects''' ==