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Module code and Title
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DTS208TC Data Analytics and Visualisation
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School Title
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School of AI and Advanced Computing
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Assignment Title
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Coursework 2
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Submission Deadline
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03/Apr/2025
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Final Word Count
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N/A
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Note: Please upload the corresponding Python code screenshots for the codes section.
T1 Nationwide Visualisation of Air Quality (45 marks)
T1-1: Plot the trends of Max AQI for all states from 2000 to 2022.
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Codes
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Visualization results
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T1-2: Create a choropleth map showing the distribution of Max AQI by state for year 2022.
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Codes
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Visualization results
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T1-3: Create a visualization showing the distribution of air quality days (Good Days, Moderate Days, Unhealthy Days, Very Unhealthy Days and Hazardous Days) in California for the year 2000.
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Codes
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Visualization results
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T1-4: Please use the below form. to describe the design of T1-1, T1-2 and T1-3.
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T1-1
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T1-2
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T1-3
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Mark
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Channel (Do not just list channels. Please describe the design of them.)
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Limitation
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T2. Predictive Analysis for California (55 marks)
T2-1: Create 5 data visualisation results to show the relationships between California’s Median AQI and its influencing factors (Year (2000 - 2021), Pop_Est, Good Days, Moderate Days, Unhealthy Days).
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Codes
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Visualization results
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T2-2: Based on the visualisation results, describe the relationship between these influencing factors and the Median AQI. Using these relationships and the 2022 influencing factor data for California, predict the Median AQI for California in 2022 without relying on model training. Justify the reason of your prediction.
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Year
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Pop_Est
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Good Days
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Moderate Days
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Unhealthy Days
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Relationship
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Prediction
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Reason
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T2-3: Train a regression model using California’s data from 2000 to 2021. The model should aim to learn the relationships between Median AQI (target variable) and its influencing factors (Year, Pop_Est, Good Days, Moderate Days, Unhealthy Days). Choose 2 evaluation metrics to evaluate your model and discuss the result.
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Codes
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Evaluation results
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Discuss
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T2-4: Predict California’s Median AQI for 2022 using the trained model.
T2-5: Compare the results of the visual prediction from T2-2 and the model-based prediction from T2-4. Discuss the differences and explain which approach you find more reliable and why.
• Comparison and Discussion
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Comparison and Discussion
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