Pre-requisite python packages
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- numpy
- cvxopt
- matplotlib
- mlxtend

Python files
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> generate_random_data.py
    - generates the Class A and Class B datapoints with proper label according to project specifications
> mnist_reader.py
    - loads the MNIST training and test data using mlxtend. Replaces 0 labels with -1 value for uniformity
> svm_model.py
    - implements the support vector machine model through SVM class definition taking C and dataset name as initializing attributes
> linear_regression_model.py
    - implements the linear regression model through LinearRegression class definition taking learning rate and maximum number of iterations as initializing attributes
> logistic_regression_model.py
    - implements the logistic regression model through LogisticRegression class definition taking learning rate and maximum number of iterations as initializing attributes
> run.py
    - executes the main function, generates necessary test cases or experiments for project requirements and draws the plots

How to run the code
-------------------
> Command: python run.py
> Input: None
> Outputs:
    - run.out: all the results needed for the report, i.e. misclassification, generalization and leave-one-out cross validation errors and margins for SVM
    - *.pdf: the plots for SVM for different C values, linear regression and logistic regression on dummy dataset
	
Report File
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> report.pdf