Description: Using the requests library, download all the people in the Star Wars universe using the Star Wars API (https://swapi.dev/documentation). Show the name of the oldest person (or robot or alien) and list the titles of all the films they appeared in. The Requests Package The requests module allows us to send HTTP requests using Python. The HTTP request returns a Response Object with all the response data (content, encoding, status, etc). »
Description: Train a deep learning model to classify beetles, cockroaches and dragonflies using these images. Note: Original images from https://www.insectimages.org/index.cfm. Insect Image Dataset The original dataset is found at the Insect Image websites. There are total 1199 images of beetles, cockroaches and dragonflies. In this project, we will train a neural network model to classfy these three different types of insects. So first let’s take a look at these insects. »
Description: This is the final project for Duke BIOS-823-2020. Brief Introduction An intensive care unit (ICU) provides intensive treatment medicine for patients with severe and life-threatening illness and injuries, or those directly transferred from emergency department. ICUs have higher sta-to-patient ratio then normal wards to provide intensive care and comprehensive monitoring to severe patients, and hence generate a massive amount of electronic healthcare records (EHR) which are useful to predict patients’ disease status and the amount of healthcare needed. »
Description: Play with PhDs awarded in the US dataset. Do some analysis in pandas. Make a dashboard visualization of a few interesting aspects of the data using streamlit. About Dataset Data presented in Doctorate Recipients from U.S. Universities: 2017 were collected by the Survey of Earned Doctorates (SED). The Survey collects data on the number and characteristics of individuals receiving research doctoral degrees from U.S. academic institutions. There are 72 tables in total in this dataset. »
LINEAR SVM Due Date : 9/28 Monday 10:15 PM EST import numpy as np import matplotlib.pyplot as plt import scipy.io as io import libsvm from libsvm.svmutil import * import pandas as pd %matplotlib inline 3.1 Linear Support Vector Machine on toy data 3.1.1 Generate a training set of size $100$ with 2D features (X) drawn at random as follows: X_{neg} $\sim$ $\mathcal{N}$($[-5, -5]$, 5*$I_2$) and correspond to negative labels (-1) X_{pos} $\sim$ $\mathcal{N}$($[5, 5]$, 5*$I_2$) and correspond to positive labels (+1) Accordingly, $X = [X_{neg}, X_{pos}]$ is a $100\times2$ array, Y is a $100\times1$ array of values $\in {-1, 1}$. »