https://www.nicholaskho.me/home daily 1.0 2021-06-29 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623085852944-ZOSWKCP42ZMIP7RS3QTC/HumbleGroundsPhotographyUCSDGrads2021-57.jpg Home https://www.nicholaskho.me/cooking-mama daily 0.75 2020-05-25 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1590379214515-G1L3SKX4G0W6A6DFAHXA/Cooking+Mama+-+Slide.jpg Cooking Mama - Cooking Mama Web Development Project UC San Diego (COGS 120 – Interaction Design Course) https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589782559540-UXJIGCNA7379VUU3KU9L/storyboard+1.PNG Cooking Mama Storyboard 1 Helps user decide what to eat based on what user has recently eaten. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589782604021-3JG7Z4TOJ5ZWQL75T5OW/storyboard+3.PNG Cooking Mama Storyboard 2 Text to speech function allows user to cook without touching phone. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589782586709-AE6PA26P8WOEZFYXAJ8U/storyboard+2.PNG Cooking Mama Storyboard 3 Inexperienced cook learns a new recipe from the app. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589783555229-E5VVZK6FHEUH7QKF458Y/prototype+1.PNG Cooking Mama Iteration 1 A more basic design that included the functions of the app, but UI was not heavily considered https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589783687479-2YITBO2D9T62EHH798Z9/prototype+2.PNG Cooking Mama Iteration 2 After figuring out how we want to layout the app, and where to put its functionalities, we focused on the design of the app. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589784303893-KCRUGBEV9S66SOTLAM42/wireframe+1.PNG Cooking Mama Home Page A preset list of food items the user could choose to cook from https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589784332113-CADXVTSR6ZGRB8QVIKXV/wireframe+2.PNG Cooking Mama Recipe Page Displays everything that is necessary for the user to cook the dish https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589784751166-TG43J1GSRJ9W4WLCFBLA/usertest+1.PNG Cooking Mama We tested the user on any slips they might have on the home page https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589784785377-I4LB64E9X2FNWDM854LO/image-asset.png Cooking Mama Testing the user on how easy it was to add a recipe https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1589784799396-RUP2PBALLAQE0EOR1F3N/usertest+3.PNG Cooking Mama Our team wanted to test how practical our app is, in a real environment https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1590379879770-A8UZJVW37FUFPFQAV6RZ/ganalytics1.JPG Cooking Mama https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1590380011736-F3LF66PXN9W4ZM8ZZG8T/image-asset.jpeg Cooking Mama https://www.nicholaskho.me/new-page daily 0.75 2020-10-02 https://www.nicholaskho.me/new-page-1 daily 0.75 2020-10-02 https://www.nicholaskho.me/generatedalbumart daily 0.75 2020-10-02 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1601680784505-LYWFODWE2C0SPU0JAIQ6/gan1.jpg Computer Generated Album Art https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1601681075790-W8PCNNXF7Y8PZJRP6OMF/gan2.jpg Computer Generated Album Art https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1601681606755-XHPQDUCF0B3TCS3P02S8/gan1.jpg Computer Generated Album Art Here is the 100th step, as the model began to train on the dataset. The results were as expected, there is not much character or variety as the model just started to train. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1601681665124-L3SOZ2ZO1BUVZI6R2Z5O/gan2.jpg Computer Generated Album Art Here is the 1000th step, as the model began to produce better and more recognizable results. There is a lot more variety, color, and abstract shapes which we hoped would develop better over time. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1601682027879-K7S46BJFNAOCRAH571XL/gan3.gif Computer Generated Album Art https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1601682346204-GR2Z7JM24E7CUNIMVNEM/gan4.gif Computer Generated Album Art https://www.nicholaskho.me/infectionriskcalculator daily 0.75 2021-04-13 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1618331097726-6LNXG54O8EL8586FR5Q0/morawska2020.jpg Covid-19 Infection Risk Calculator Where Ni is the droplet concentration by diameter. These droplet concentrations were taken from Morawska et al. 2009. Cv corresponds to the viral load in the sputum (i.e. infectious mucus) of the infected person. The viral load in the sputum of an infected person varies and is dependent on the stage of infection so for this estimator we will choose a large viral load of 1e9 RNA copies per milliliter to simulate the worst case scenario for infection spread [based on Walsh et al. 2020 ]. IR (Inhalation Rate) the product of breathing rate and tidal volume, expressed in cubic meters per hour. IR is dependent on the activity of the infected subject. We chose IR values from Adams 1993 . https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1618331239176-OS2QC98YA3JGWRJXE15U/gammaitoni+and+nucci.jpg Covid-19 Infection Risk Calculator IVRR (hr−1) represents the infectious virus removal rate in the room. The IVRR is the sum of three parameters: the air change per hour (ACH) via ventilation, the particle deposition on surfaces via gravitational settling which is 0.63 according to Doremalen et al. 2020, and the viral inactivation rate which is 0.24 per hour according to Chatoutsidou and Lazaridis, 2019. n0 represents the initial number of quanta in the space, we presume there is no initial quanta, which means n0 = 0. I is the number of infected people. V is the volume of the indoor environment considered. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1618331340386-ISSDUEC5SRM29729F824/Risk+equation.jpg Covid-19 Infection Risk Calculator https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1618331509020-QK4JC12BP8XBEPILBYYA/blank+calc.JPG Covid-19 Infection Risk Calculator https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1618332135440-880522FDCGKCPS0FNOU0/filled+calc.JPG Covid-19 Infection Risk Calculator https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1618332314346-AUKG1FLGJ4TYQPL7N4QL/custom+calc.JPG Covid-19 Infection Risk Calculator There is a set layout you need your csv to be in for the calculator to properly read your data https://www.nicholaskho.me/snapchat daily 0.75 2021-06-07 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623087929124-IDTFMBFVNRG3P9J8ZXEY/snapchat.jpg Snapchat - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623097307307-OU6BM4V00C34O04SFOVZ/baselinept1.JPG Snapchat - Make it stand out Initializing and combining political ad data from 2018 and 2019 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623099349923-AB8XCFNQKGHNH030MEB9/baselinept2.JPG Snapchat - Make it stand out Clean out unnecessary features such as ‘ADID’ and ‘CreativeURL’ and turn ‘Impressions’ into our y-values https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623101825252-8XWB5C7KK7U8IT1GT18V/baselinept3.JPG Snapchat - Make it stand out Creating the categorical columns and numerical columns https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623101924417-8QILA4JVKXN107N9NQ5I/baselinep4.JPG Snapchat - Make it stand out Creating pipeline that takes in categorical data which imputes values, and one hot encodes the data. Then combines the categorical and quantitative features. Finally I run linear regression on the new dataset features. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623102239979-MZ2JMBZH1G7LBVDEPZQG/baselinept5.JPG Snapchat - Make it stand out Splitting the Dataset into a training set and test set https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623102379445-KTUP5NW79GFQ3732QF95/baselinept6.JPG Snapchat - Make it stand out Getting the score for the baseline model, which is terrible https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623102450034-WU5PQXFLOEJUCZ5SR987/baselinept7.JPG Snapchat - Make it stand out The root mean square error of the baseline model, which is also terrible https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623102698252-FBC8GGSWAGHWOXIMGJO5/finalpt1.JPG Snapchat - Make it stand out Initializing and combining political ad data from 2018 and 2019 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623102812030-KIQS6HQV0WFFJOGJAPDM/finalpt2.JPG Snapchat - Make it stand out Getting the median from each year to use to later normalize the data https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623103063550-99WIVP9BHPUPANJPTI1M/finalpt3.JPG Snapchat - Make it stand out Converting StartDate and EndDate to datetime object https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1623103150057-Z4UQ3X4Y4ZVWNGT7DXL1/finalpt4.JPG Snapchat - Make it stand out Using the median to normalize the data because 2018 has much higher spending costs due to midterms https://www.nicholaskho.me/lcsviz daily 0.75 2021-06-30 https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1624998752578-B0NV2YZSRERC4VHX4T9R/League_championship_series_logo_2021.svg.png LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1624999311274-6FR42DAFJISNHOC9IE2Q/vis1.JPG LCSViz - The data I found had a .csv for every event for every split (eg. Playoffs Spring 2020, Summer Split 2020) and the data dates from 2013 to 2021. All in all, there were 18 files I had to reconcile. This is what Spring 2020 looks like: https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1624999587617-M3IJQ668VI7AJYD206T4/vis2.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625000294708-UCWZDI27E86E9VDRH1US/vis3.JPG LCSViz - I created a function that would loop through each split and create a dictionary that would have the name of the split as the key and the values would be a list which has the Champion as index 0 and games played as index 1. (Eg. {‘spring_2020’: [[‘Aatrox’, 46], etc.]) https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625000641313-D0NCINQ1SJQFFRGM0SKB/vis4.JPG LCSViz - This is the resulting data frame after Table1. The cleaned data frame showing games played per Champion in each split https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625000963595-BMILX09GULTT0VK6ASQ0/vis5.JPG LCSViz - Having the ability to see the top 5 most popular champions each split gives a good picture of where the meta is at and helps us analyze to see if new champions are really dominating the meta during the year they are released. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625001572556-WRGMV41Y7J8ZNSQRNU9D/vis6.JPG LCSViz - I needed to collect the dates on which every champ was released, unfortunately, there was no dataset for that, so I scraped: https://leagueoflegends.fandom.com/wiki/List_of_champions https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625001733495-43ROAHV1VJS0UWJJG5TR/vis7.JPG LCSViz - Make it stand out Table2. Data Frame of Champions created in each year https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625076975810-GFICOPUYF39JKCOTCGQC/vis8.JPG LCSViz - Create a function that will take in the role and return a dictionary that has Champions as the key, and the games played in that specific role. (eg. input ‘jungle’ as role = {‘Lillia’: 59.0, ‘Nidalee’: 197.0 ,etc.}) https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625077685606-WHZWFSWTB697GXELB6K4/vis9.JPG LCSViz - Now we just have to sort the dictionaries by descending and turn them into a data frame for easier reading Resulting Data Frames of the top 5 most popular champions in each role: https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625077811251-79OVW4ZFKLO7F8RVYUF9/vis11.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625077765084-5MJRKAWFEWZZB0KUGF2Q/vis10.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625077931949-TEIQ8OK36A4I85RRVSRN/vis12.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625077948009-KMTE378L3V8DGCHKBNV6/vis13.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625078126717-P4FO3O8F9775HM6658AC/vis14.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625078916235-T2B8090BWO31DRAXL8RD/vis15.JPG LCSViz - I created a function that takes in the Champion name and the Role and reads the cleaned data frame and finally outputs what would be the percent of games the Champion has won in that role. (Eg. get_WL(‘Renekton’, role): returns 0.51) https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625079447635-VFAGQEW9ICDEBLQCEI08/vis16.JPG LCSViz - Using the most played champions per role data frames I am able to create a table that displays popularity and performance statistics. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625080131910-ZPQ7KFHKB5BKWIQSGS2J/vis17.JPG LCSViz - First, I have to start by mapping each champion to a year and create a new dictionary for that information. So We start by looking at the Champions in Table1 and map the Champion to the year they were made by matching them to the data in Table2. The resulting dictionary is nested, the first key is the name of the split and the next key is the year champions were created and the value is a list with games played from each champion from that year The output looks like this: {‘summer_2021’:{‘2013’:[1,6,2,12,9],etc.]}} https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625080629503-BKUJQBCARO315VVXJCO5/vis18.JPG LCSViz - Now I just have to sum up all the values in the dictionary to get the number of games played from champions in each year in every split. Output: {‘summer_2021’:{‘2013’:30, etc.}} https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625080797296-N3SSWORUXVQJDEIZX0YX/vis19.JPG LCSViz - One issue we run into with using the sum, is that the every year has a different amount of champions made. During Riot’s most prolific era, 2009, they made 41 champions, while in 2018 they only made 3 new champions. So to make this fair, we take the average of games played to champions made. https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625082033275-HZ1080E29NNJH95XMC5A/vis20.JPG LCSViz - Make it stand out Table 3. Displays the average games played from every champ created in each year over the splits https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625082373225-0CMVAILYHQJXMD1763IB/vis21.JPG LCSViz https://images.squarespace-cdn.com/content/v1/5e9a439a89a9cf6af89f3e3b/1625082490049-RWCJDI4YR52M5L0R5Y6N/vis22.JPG LCSViz - Make it stand out Whatever it is, the way you tell your story online can make all the difference.