| Category | Assignment | Subject | Computer Science |
|---|---|---|---|
| University | Coventry University | Module Title | 7035SSL Data Mining Methodology & Applications |
The assignment is a 02 hours Phase Test that will help us to assess your ability to handle a dataset, make necessary preprocessing steps, select suitable model and apply data mining techniques to deduce the results and present in terms of visualisations. The work should be origional and submitted during the on-camera 02 hours' time. Data will be released just 5 minutes before the test. You need to analyses the data and answer below tasks
1. Data preprocessing: Load the dataset into a suitable data structure and perform necessary preprocessing steps such as missing value imputation, normalization, and feature selection
2. Model selection: Select an appropriate regression model for predicting the median value on the input variables. You should compare and contrast at least two different regression models, such as linear regression, polynomial regression, ridge regression, or Lasso regression, and justify your choice of model based on its performance and interpretability.
3. Model evaluation: Evaluate the performance of your chosen regression model using appropriate performance metrics such as mean absolute error, mean squared error, or R-squared. You should compare the performance of your model to a baseline model, such as a model that always predicts the mean value of the target variable.
4. Data visualization: Use appropriate data visualization techniques such as scatter plots, histograms, and box plots to gain insights into the data and identify patterns and trends. You should also create visualizations that compare the target variable to the input variables and explore any relationships or correlations between them.
5. Conclusion: Summaries your findings and discuss the practical implications of your regression model for end-users such as managers or policy makers. You should also suggest future research directions for improving the accuracy and usefulness of the model.
The Learning Outcomes for this module align to the marking criteria which can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task:
1. Critically select data mining methods and their applications in business.
2. Critically apply data mining methods in business cases.
3. Create effective data mining models to support business decision-making.
4. Analyse critically and interpret the outputs of data mining models for the end-user
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