ML Classifiers

Naive Bayes on the MNIST dataset. Classification accuracy = 84.03%
Perceptron on the IRIS dataset
Implementation and comparison b/w Maximum Likelihood and Expectation Maximization
Implementation and analysis of Bayes' classifier for high-dimensional data
Analysis of density estimation using Expectation maximization for unsupervised clustering
Understanding effect of different classification methodologies on synthetically generated data (sample from: Gaussian/Gamma/Uniform distributions)
Data exploration and analysis of the German Credit Score dataset for classification
Implementation of sub-sampling and oversampling strategies (in particular: SMOTE) for effective classification on the imbalanced German statlog dataset.
Analysis and verification of classifier performance on the IRIS dataset (ROC curves, decision boundaries, etc.).
Observing overfitting using polynomial regression by adjusting model complexity.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
Bayes Classifier.ipynb		Bayes Classifier.ipynb
Density Estimation.ipynb		Density Estimation.ipynb
German Statlog - Classification with sampling strategies.ipynb		German Statlog - Classification with sampling strategies.ipynb
German Statlog - Exploration & Classification.ipynb		German Statlog - Exploration & Classification.ipynb
IRIS Classification.ipynb		IRIS Classification.ipynb
IRIS ROC Curve.ipynb		IRIS ROC Curve.ipynb
Maximum Likelihood and Expectation Maximization.ipynb		Maximum Likelihood and Expectation Maximization.ipynb
Naive Bayes on MNIST.ipynb		Naive Bayes on MNIST.ipynb
Perceptron.ipynb		Perceptron.ipynb
Polynomial Regression.ipynb		Polynomial Regression.ipynb
README.md		README.md
Synthetic Data.ipynb		Synthetic Data.ipynb

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