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  Refer link:  Keyword on Google: bad data visualization examples bad dashboard examples powerbi link 9 Bad Data Visualization Examples That You Can Learn From - https://www.gooddata.com/blog/bad-data-visualization-examples-that-you-can-learn-from/ Bad Data Visualization: 9 Examples to Learn From -  https://www.luzmo.com/blog/bad-data-visualization Dashboard examples: The good, the bad and the ugly -  https://www.matillion.com/blog/dashboard-examples-the-good-the-bad-and-the-ugly Power BI Dashboard Design: Avoid These 7 Common Mistakes -  https://zebrabi.com/power-bi-dashboard-design/ reddit discussion https://www.reddit.com/r/dataisugly/search/?q=poor+data+visualization+examples&cId=c9ce09ee-ddf4-40c4-8a97-82924c068004&iId=99a2075f-2971-4e17-8c45-ab4557e4c16e

Load data from default repo Just open the command prompt and type code as below > python Python 3.12.2 (tags/v3.12.2:6abddd9, Feb  6 2024, 21:26:36) [MSC v.1937 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information.     >>> import sklearn >>> print(sklearn.__version__)   1.6.1   >>> from sklearn import datasets >>> dir(datasets)   ['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__getattr__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_arff_parser', '_base', '_california_housing', '_covtype', '_kddcup99', '_lfw', '_olivetti_faces', '_openml', '_rcv1', '_samples...

  Truncate to reduce

 SELECT * FROM sys.dm_tran_locks; SELECT creation_time, total_rows, text FROM sys.dm_exec_query_stats qs CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) order by creation_time desc SELECT      blocking_session_id AS BlockingSession,     session_id AS BlockedSession,     wait_type, wait_time, wait_resource FROM sys.dm_exec_requests WHERE blocking_session_id <> 0;

 Since your dataset was reduced from all attributes to only two (Status & Target) , you should compare the evaluation metrics (e.g., accuracy, precision, recall, F1-score) of the classification models before and after reduction. 1. Collect Evaluation Results from Weka After running classification models on both datasets ( full dataset and reduced dataset ), record the following metrics: Accuracy (%) Precision (%) Recall (%) F1-Score (%) Cross-validation (k=10, k=20) results Percentage split (70:30, 80:20) results 2. Organizing Data in Excel Step 1: Create a Table in Excel Format your results into a table: Dataset Model Accuracy (%) Precision (%) Recall (%) F1-Score (%) Full (Before Reduction) J48 85 84 83 84 Full (Before Reduction) Random Forest 88 87 86 87 Reduced (After Reduction) J48 75 74 73 74 Reduced (After Reduction) Random Forest 78 77 76 77 Step 2: Insert a Bar Chart Select the table data. Go to Insert →...

Sam Kas Co is a shipping company that owns six ships. In emergency cases, the company will release the coordinates of each ship to other ships. The nearest ship will be assigned to help the trouble ship. Assume now that a ship. MayDay with coordinate (10,5), is in trouble. Table below shows the current coordinates for all the other ships.   Ship Name Coordinate X Coordinate Y Air 18 11 Bay 7 1 Cell 3 7 Dream 11 4 Eve 6 6   1.       Write a pseudocode that receives the coordinate of the troubled ships and a list of other ships’ coordinates. The algorithm will return coordinate that of the nearest ship to the troubled ship.   FindNearestShip(troubleShipX, troubleShipY, ship_list)       minDistance = INFINITE   ...