Built a generative model to mimic the writing style of prominent British mathematician, philosopher, prolific writer, and political activist, Bertrand Russell.Trained a LSTM that mimics Russel's style and thoughts.

Used a convolutional neural network for image colorization which turns a grayscale image to a colored image. By converting an image to grayscale, we loose color information, so converting a grayscale image back to a colored version is not an easy job. I used the CIFAR-10 dataset.

scikit-learn,pandas,keras,tenseorflow

Multi-class and Multi-Label Classification Using Support Vector Machines on Anuran Calls dataset and K-Means Clustering on a Multi-Class and Multi-Label Data Set
Approaches:Monte Carlo simulation, using linear, Gaussian kernels kernel and L1-penalized SVMs, SMOTE, CH or Gap Statistics or screen plots

Supervised, Semi-supervised and unsupervised learning on Breast Cancer Dataset
Approaches:Monte Carlo simulation, supervised, Clustering,Spectral Clustering, Self Training and L1-penalized SVMs, SMOTE, CH or Gap Statistics or screen plots

Binary classification problem on banknote authentication dataset

Approaches:

Communities and Crime dataset, APS Failure dataset
Approaches: Data imputation techniques, linear,ridge regression, PCR models , boosting tree, multivariate regression tree, L1 penalized gradient boosting tree, XGBoost, random forest, Out of Bag error estimate, ROC, AUC, Weka, SMOTE

An interesting task in machine learning is classification of time series. In this problem, I tried to classify the activities of humans based on time series obtained by a Wireless Sensor Network.
Approaches: Time-domain features, bootsrap confidence interval, binary classification Using Logistic Regression, p-values, backward selection using sklearn.feature selection, stratified cross validation, Python's Recursive Feature Elimination, ROC, AUC, L1-penalized logistic regression, L1 regularization, L1- penalized multinomial regression, Naive Bayes' classifier using both Gaussian and Multinomial priors

cycle power plant dataset(arem)
Approaches: Scatterplots, box plots, Classification using KNN, Learning curve, Euclidean, Minkowski, Manhattan, Chebyshev, Mahalanobis distances, simple linear regression model, association of interactions of predictors with the response using p-values, KNN Regression.

cycle power plant dataset(arem)
Approaches: Scatterplots, box plots, Classification using KNN, Learning curve, Euclidean, Minkowski, Manhattan, Chebyshev, Mahalanobis distances, simple linear regression model, association of interactions of predictors with the response using p-values, KNN Regression.