Visual Data Analysis Assignment Help

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CST4060 Visual Data Analysis Assignment

Assessment Title - Visual Analytics Workflow Design

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Assessment Task - Choose a dataset from the UCI machine learning repository and design a visual analytics workflow in KNIME to process, analyse and visualise the data.

Answer: The UCI Machine Learning Repository is a vast collection of datasets used for machine learning research.

These datasets span various domains, from healthcare and finance to social sciences and engineering. KNIME, a visual analytics platform, offers a user-friendly interface to process and analyze these datasets. A typical workflow in KNIME for processing UCI datasets involves:

Data Ingestion: Importing the dataset from the repository into KNIME using the "CSV Reader" or "Excel Reader" node.

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Data Exploration: Examining the dataset's structure, distribution, and missing values using nodes like "Data Statistics," "Histogram," and "Missing Value Analyzer."
Data Cleaning and Preprocessing: Handling missing values, outliers, and inconsistencies using nodes like "Imputer," "Outlier Detection," and "Normalizer."
Feature Engineering: Creating new features or transforming existing ones to improve model performance using nodes like "One-Hot Encoder," "PCA," and "SMOTE."
Model Building: Selecting and training appropriate machine learning models (e.g., decision trees, random forests, neural networks) using nodes like "Decision Tree Learner," "Random Forest Learner," and "Deep Learning."
Model Evaluation: Assessing the model's performance using metrics like accuracy, precision, recall, and F1-score, and visualizing results using nodes like "Confusion Matrix" and "ROC Curve."
Deployment: Integrating the trained model into applications or workflows for real-time predictions or decision-making.

Comprehensive Data Models Assignment Assistance: Delve into a detailed comparison of database systems versus file-based systems, while exploring the advantages and limitations of various database models for a deeper understanding and practical insights.

The workflow should contain the following:

1. Component(s) for data processing.

2. Component(s) for data mining.

3. Component(s) for visualizing the data and/or data mining results.

Justify your design by:

1. Explain what is the goal (research question) of your analysis.

2. How does your workflow help achieve the goal.

3. Interesting findings achieved by apply the workflow on the data.

Note - Submit your report to Unihub. The report should include the screenshot of your VA workflow and text and screenshots that justify your design.

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