Difference between revisions of "Fall 2024: Data Mining Lab"
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| 2 || https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html || Practice Set No. 2 || | | 2 || https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html || Practice Set No. 2 || | ||
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| + | == '''Lab 1:''' ( week of 12<sup>th</sup> August 2024 ) == | ||
| + | {| class="wikitable" style="text-align: justify; width: 100%"; | ||
| + | |- | ||
| + | ! Q. NO. | ||
| + | ! Program | ||
| + | ! Practical No. | ||
| + | ! Remarks | ||
| + | |- | ||
| + | | style="width: 8%" | 1 | ||
| + | | style="width: 60%" | 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. | ||
| + | | style="width: 15%" | Practical No. 1 | ||
|} | |} | ||
Revision as of 12:17, 9 August 2024
Contents
Instructions
- 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.
Guidelines
- As per DUCS guidelines (will upload soon..)
Lab 0: Getting Started ( week of 04th August 2024 )
| Q. NO. | Program | Practical No. | Remarks |
|---|---|---|---|
| 1 | https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial1/tutorial1.html | Practice Set No. 1 | |
| 2 | https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html | Practice Set No. 2 |
Lab 1: ( week of 12th August 2024 )
| 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 |
