B9FA101 - Data Analytics & Machine Learning for Finance - IT Assignment Help

Assignment Task

Question 1:
Use a real-world dataset (i.e. relational, text, image, video, voice files), prepare the dataset for modelling, consider one categorical variable in the dataset, and apply the classification task. To do so:
Provide the functional form of the predictive model for each algorithm.

Train each model using different ratios of the trainset and visualize the performance of models using accuracy (y -axis) in terms of different ratio of trainsets (x-axis). Elaborate on the insights.
Apply ensemble methods (bagging, boosting, stacking) on the base models, evaluate the performance of each ensemble technique in 100 Monte Carlo runs and visualize the performance of models using Boxplot.
Select the best classifier and elaborate on its advantages and limitations.

Question 2:
Consider a continuous attribute in your dataset as the target variable, perform regression analysis using different ensemble methods, visualize and interpret the results.

Question 3
Use dataset classify the dataset into few classes so that at least 90% of information of dataset is explained through new classification. (Hint: model the variable “qtr” to variables “togo”, “kicker”, and “ydline”). How many LDs do you choose? Explain the reason.
Apply PCA, and identify the important principle components involving at least 90% of dataset variation. Explain your decision strategy? Plot principle components versus their variance.

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