15 lines
2.7 KiB
Markdown
15 lines
2.7 KiB
Markdown
#rs/class/ad450 #rs/discussion
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1. **Personal Experience with Data Visualization:**
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- Describe an instance where you have used data visualization in a project or a task. What was the purpose of the visualization, and what tools did you use to create it?
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- One project that I have used data visualization for is a machine learning project for one of my other classes. I talked about this project in a previous discussion and there were several places where I used data visualization to get a better idea of the data I had and use it to make better decisions on preprocessing. One of the visualizations that I used was a pairplot which shows scatter plots for each combination of features in the dataset. It will also color the dots by the output class which can be very helpful to see which features might have correlations that could help to predict the output class or that might interfere with predictions. When used in combination with a correlation heatmap between all the features correlation can be found. Using this I was able to identify several features that were essentially duplicates and would not provide any more information to the model. To create these visualizations I used matplotlib and seaborn. These libraries are very helpful for creating visualizations of data, especially when also using pandas.
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2. **Challenges and Learning:**
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- What challenges did you encounter while working on your data visualization? How did you overcome these challenges? Share any learning points or insights you gained from this experience.
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- One of the challenges that I encountered while creating the pairplot visualization was the amount of data that I was displaying. The dataset that I was using has over 100k instances and since the pairplot was graphing a scatterplot between each pair of features it would take a very long time to generate. To make it faster to create I randomly sampled a much smaller number of the instances to still get a good idea of the distribution and relationships between the features while reducing computation time.
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3. **Impact of Visualization on Understanding:**
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- Reflect on how the visualization helped in better understanding or communicating the data. Was there a notable difference in comprehension or decision-making due to the visualization?
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4. **Tool Exploration:**
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- Have you experimented with any data visualization tools (like Tableau, PowerBI, Microsoft Excel, Google Sheets, RStudio, Jupyter Notebook etc.)? Describe your experience with these tools. Which one did you find most effective, and why?
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5. **Future Applications:**
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- How do you envision applying data visualization techniques in your future projects or career? Discuss any specific areas where you think data visualization can be particularly impactful. |