This project is third in the series of Hypothesis testing project whereby we use Bayesian Methods. Here we reformulate the AB testing problem into MultiArm Bandit and implement 4 widely used algorithms: Epsilon Greedy, Optimistic Initial Conditions, UCB1, and Bayesian (Thompson) Sampling for the AB testing. We then compare and contrast the performance of the algorithms wrt the Bandit problem....
In this project, we observe distributions of a read world data. The data comprises of Stock prices of "Google" from year 2014-2018. We will compare normal and t-distributions to fit the observed values. Finally, we will try Mixture of Gaussians to fit the data and note scope and limitations....
This project implements feature generation and matching using OpenCV library. We observe two classes of object types: The first is solid cube shaped object such as a cereal box that is ideal for homography transforms and post processes. The second is deformable object such as a bag of chips that can be physically distored between two images and homographies and post processing may fail on them. In this project we observe the usage of optical flow on the 2nd category to check viability of estimating such distortions....
In this project I implement AB test in numpy. I compare the result with scipy's ttest results. This is an exercise in hypothesis testing and we will still be using scipy t & chi squared distribution class to get the t and chi squared cumulative distribution function. The t tests can be one or two sided depending on the parameter input....
In this project I test SVM on facial expression data: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge. Here I only deal with two label classes: Happy and Angry....
In this project we begin unsupervised ML with K-means clustering....
In this project we employ Sklearn's pipelines to compare performances two robust models for NLP : Multinomial Naive Bayes and the Random Forests....
In this project we utilize SVM to evaluate ML performanceson IRIS image dataset. The Data: Iris flower data set. The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), so 150 total samples. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. Here's a picture of the three different Iris types:...
In this project we try out the most common Nearest neighbors technique: K-NN....
In this project we utilize Decision Trees and Random Forest to evaluate ML performances. Data: Lending data from 2007-2010 (https://www.lendingclub.com/info/download-data.action) to classify and predict whether or not the borrower paid back their loan in full....
