Assessing the capacity of data visualisation

Assignment Help Database Management System

Reference no: EM132542106

Learning outcome 1: Assessing the skill of starting a data analytics process from A to Z.

Learning outcome 2: Assessing the ability of data analytics and model creating.

Learning outcome 3: Assessing the capacity of data visualisation and result in communication.

Assignment Task

Your task is to create a visualisation of carpark occupation rate for all carparks to identity which one (or which groups) is/are reaching the margin of full capacity very often and thus those should be the first priority in the next investment for expanding the capacity. From the visualisation result, you are expected to propose two analytics goals. For example, you can identify two carparks, which are potentially having high correlation on occupation rates, and build the prediction models for these two. Or, find two carparks, which are potentially having the negative correlation and build the prediction models for them. A correlation analysis can help you to judge whether the picked carparks are actually related to each other. Finally, a report should be provided at the end for the submission of this task.

Your proposal must address the following tasks:

Part 1. Visualisation of all carparks' occupation rate. (Radial chart, bar chart, etc. are all good choices for visually presenting the results.)

Part 2. A correlation result of your-own chosen two carparks. It's better to get a pair with the strong positive/negative correlation.

Part 3. Two prediction results of occupation rate for the selected carparks in the previous step. You should perform the process of data partitioning and use the training set for the model training and use the test set for verify the accuracy. Use at least one evaluation matrices such as Root Mean Square Error (RMSE), Standard Deviation (SD) to evaluate your model.

Part 4. Identifying the potential missing attribute in the data collection phase and justify why you think those data should also be collected and why those attributes can help in the data analytics.

Part 5. A comprehensive report

Attachment:- data analytics process.rar

Reference no: EM132542106

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