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“It’s always about timing. If it’s too soon, no one understands. If it’s too late, everyone’s forgotten.” — Anna Wintour Trends is an analysis of collecting information and attempting to spot a pattern. In an e-commerce setup like 1mg, an item or product is trending when it is interacted with(purchased, clicked, add to cart, viewed, searched, etc.) more often than usual. What is Trends? How can one capture trends- For your easy read, we are going to structure this blog in the following parts: Part 1: In this part, we will discuss the objective of showing trending items and products to the users by answering the following questions: Who is the target audience? What is the difference between trending products and top-selling products? This will help us understand what ‘Trends’ really are. Part 2: In this part, we will discuss the strategy/algorithms involved. Part 3: In this part, we will discuss the drawbacks of our strategy. Part 4: In this part, we will discuss the challenges we faced while working on ‘Trends’. Part 5: In this part, we will share concluding remarks. I. Understanding Trends Ideology If we ruminate, here are the people we are trying to show the trends to:• : Users can utilize Trends in multiple ways-One is staying of growing occurrences of conditions or diseases so that they can take preventive measures. Another could be that these trends could help them gain knowledge about the conditions or diseases.• Trends could be sudden spikes that are difficult to assess based on past demand. Therefore, If demand for a particular item is growing it will help concerned sellers/vendors can be . Customers aware Sellers: prepared with adequate stock There are some additional benefits of showing trending products: It is difficult for brands that are introducing new products to enhance visibility for their catalog against the market that is swarmed with popularly purchased and highly viewed goods. A trending list will also bring . Brands could improve product visibility by coming up with different attractive offers to increase demand. competition to the market Diversity is one of the objectives of any E-Commerce Recommendation Engine. Changing seasons makes different products relevant and instead of developing rules to identify relevant products for various seasons, trends could be used to come up with interesting recommendations and thereby For example Masks, Giloy, Sanitizer, Tulsi Drop, etc. are currently trending among people to fight Coronavirus. bring diversity through seasonality. In order to have a clear understanding of trends, it is important to note the difference between trending products and top-selling products. To get a better understanding of trends, it is important to get the difference between trending products and top-selling products. The table below lists some common differences which make these two ideologies come up with different products altogether. II. Strategy/ Algorithm: Rolling-Zscore To find the trendiness of a product one can take such as Add To Cart, Product Page Views, Product Sales, etc., or can develop a weighted function of these indicators. To keep things , we take as an indicator to define the trendiness of a product. Strategy to find Trending Products? indicators simple and highly accurate product sales Trending is equivalent to an anomaly detection where there is a sudden change from the usual pattern. Indicates the change in the current week’s sales from past weeks’ mean sales by a number of standard deviations in the window of size . Methodology: Rolling-Zscore/ Moving-Zscore: w w Rolling Z-Score where the (Xi) moving means and moving standard deviation (Si) is Formula: Moving Means and Std. After calculation of Rolling-Zscore, we sort items on basis of rolling-zscore and pickup (for us we have kept k=30) items from the list. top-k Rolling-Zscore is simple to implement and works in real time with low computation costs. It is a good indicator to find which product is oversold or overbought. This strategy also gives good exposure to products that had low sales in the past, resulting in improvement in the diversification of products on our platform. Top All India Trending Products for the 3rd week of March 2020. Dummy ZScore Data for the 3rd week of March This strategy of Rolling-ZScore gives us trending products that are relevant and explainable to the scenario. COVID-19 Trending List of 3rd week of March 2020 Trending List 3rd week March | During COVID-19 Trending List of 4th week of March 2020 Trending List of 4th week of March 2020 | During COVID-19 Let us try to unwrap the reasons why these products are showing up in the trending category: , help people to . Tinospora Cordifolia Mother Tincture Q Giloy Tablets, Amruth, and Tulasi drop boost their immunity sales rise because it enables people to check their . Thermometer body temperature sales rise up because of (weather change). Crocin and Paracetamol Tablets cold-flu help people to . Hand Wash and Alcohol Swab protect themselves from bacteria and viruses III. Drawbacks of using Rolling-Zscore Rolling-Zscore helps us easily identify products for which sales were previously low but there is a significant jump in the current time. Rolling-Zscore fails to give exposure to our top-selling products such as Shelcal (Vitamin D Tablet) or Neurobion Forte ( MutliVitmamin Tablet). These products would never be able to come in the trending products category even if their current sales are higher than past sales because their relative ranking as per their Z Score would be lesser than other products due to a much higher denominator. IV. Challenges faced The basic idea behind trendiness is the rate of change in sales of a product in a given time frame. It may often happen that due to stock unavailability of product it past week's mean sales are very low and when the product becomes available a sudden sales jump is observed and the z-score catches it as trending. To tackle such scenarios a proper statistical justified threshold has to be set on rolling-mean sales. The setting of a correct window size is very important. It also depends on the product call, regarding how long you would like to show a product as a trending one. Example: During the starting weeks of May, we captured prickly heat powder as trending but by the end of May the product no longer remains trending as it transits to the category of top-seller product. To capture plateau one has to tinker with the window size. w : compare 3 days to 5 days of sales with the past 10 days means sales. To capture the short-term trend : compare 1 week’s sales with the past 3 to 4 weeks’ mean sales. To capture the mid-size term trend : compare 1 month’s sales with the past 3 to 4 months’ mean sales. To capture a long-term trend V. Conclusion Today, we show this trending list in two spaces: on the homepage and on the search bar (app only). With this strategy of , we are able to find top-50 most trending products from more than 2 lakh products in just 3 minutes, allowing us with real-time trend analysis at a low computation cost. Rolling-Zscore I hope you enjoyed my blog and learned something new. Want to discuss it further? Connect with me . here My is now read by more than 4500+ subscribers. If you are building an AI or a data product or service, you are invited to become a sponsor of one of the future newsletter issues. Feel free to reach out to shauryauppal97@gmail.com for more details on sponsorships. Newsletter on LinkedIn I am nominated for the HackerNoon 2022 Noonies, Vote for me: || HackerNoon Contributor of the Year  - Data Data Science Demon Also published here. Connect 1:1 Meeting here: https://topmate.io/shaurya I am open to Consults you can reach out to me on LinkedIn. Data Science Book Recommendations: [1] The Book of Why [2] Naked Statistics

Capturing Trends in HealthCare at 1mg  (E-pharmacy Unicorn)

Abstract In the last couple of years, short videos have become the new darling of the digital mediascape. After the internet boom in India, many influencers are emerging daily. We all have our favorite creators and can spend hours watching their content. For a platform like ours, we needed a user-creator affinity recommendation model such that we recommend creator stories to users based on the affinity (likeability) factor where a consumer’s (user) likeability for a creator is defined by: Follow, Profile Visit, Like, Comment, Share, etc. Overview Affinity means a natural liking for and understanding of someone or something. Affinity is a factor that changes with time and interest niche. Our goal is to capture user-creator affinity strength, which also captures users’ i.e., what type of stories a consumer (user) prefers more. temporal interest niche Business Goals Improve stories recommendation algorithm such that user’s session time increases Improve user niche discovery for content Improve visibility of and content discovery based on consumer’s likeability factor. long-tail creators Expected Outcome Recommend a list of story ids of creators for whom user-creator affinity is high. : A creator is also a user on the platform. Hence, I will address as who watch a creator’s video. NOTE users consumers Interaction between Creator-Consumer on Roposo Out of these different interactions b/w consumer-creator, we decided to pick profile visit as a stronger signal to map out similarity between creators. Approach High-Level Approach We divided the problem into 2 parts: For each consumer (user) find the top K creators based on the for which the True Affinity score is high; where affinity is defined by like, follow, profile visit, comment, loop_count, perc_seen (percentage of video seen based on video duration), etc. Multi-Criteria Decision Making TOPSIS technique Post finding these True Top K creators for whom affinity is high based on MCDM. We take embedding of these creators (embedding computed using node2vec embedding methodology) and find out nearest neighbors and recommend similar creators. *: First, we find out true high-affinity creators for a consumer based on MCDM. Then we find similar creators with respect to high-affinity creators.* In Summary Implementation Details (TLDR) — Refer to the above Approach Figure with Steps Step 1: Finding True Top Affinity creator for a Consumer (user) from interactions. This is a as we wanted to rank all creators for a consumer (user) with whom the consumer interacted in the last 30 days. multi-criteria decision-making (MCDM) or multi-criteria decision analysis (MCDA) problem Consumer-Creator Interaction Data We use the an MCDM algorithm to rank creators in order of affinity. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the ideal solution and the longest geometric distance from the worst solution. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Scikit Criteria_:_ Link One can check out my blogs to get a detailed understanding of MCDM: Ranking of entities with Multi-Criteria Decision Making Methods (MCDM) — Part One | Ranking and Selection of the best with Multi-Criteria Decision Making (MCDM) — Part Two Post step1, for every consumer we have a ranked creator set based on affinity factors with whom the consumer has interacted in the last 30 days. Sum up: **Step2 & Step3: Creator Graph — Profile Visits to Embedding**We constructed a Creator-Creator graph based on the profile visits of a consumer. Connections between those creators were made for which profile visits by consumers co-occurred on a particular day. The graph weights were defined by co-occurrence strength (number of times profile visits by a consumer co-occurred). We computed the creator embeddings based on that uses word2vec skip-gram model. Paper Link Node2vec+ Node2vec Params: How to set p and q? The top and bottom panels correspond to the embedding generated using q = 0.5 and q = 2. One can see that in the top panel, nodes that fall into the same (i.e., ) are colored the same. On the other hand, in the bottom panel, nodes are colored the same. node2vec local network neighborhood homophily structurally equivalent Params q=0.5 and p=1 in this setting node2vec discover clusters/communities of characters that frequently interact with each other. Since the edge b/w nodes are based on co-appearances. Post Step2 & Step3, we now have creator embedding computed based on creator-creator graph build based on co-occurrence of profile visit. Sum up: **Step4: Recommending Top Creators**Now, we have true Consumer (User)-Creator Affinity Ranked based on MCDM and we have embeddings of all (active) creators on our platform. We pick the top 5 True Affinity Creators ranked from the MCDM technique and recommend Nearest Neighbours to get the top 100 high-affinity creators. Why top 5 True Affinity Creators were picked as query vectors? Why not pick the best top 1 or create a mean vector of top 5 creators and show similar creators to the query vectors in embedding space? Idea of picking top 5 creators is inspired from Pinterest Research Paper . PinnerSage It is true a user cannot be represented by one particular “interest” embedding.In general even in example of movies everyone shows interests in multiple genres likes honor, action, sci-fi, comedy, etc.To identify user interest we pick top 5 creators from the ranked set. For vector similarity search we used Approximate Nearest Neighbour Algorithm (ANN) over creator embeddings for fast vector similarity search. ScaNN Approach Summary (Recap) We use MCDM to rank creators for each consumer using the interactive features (affinity-defining features). This ranked set of creators is the True Affinity Creators for a consumer (user). Now, we create a creator-creator graph based on profile visits co-occurrence in a session of a consumer. On this graph, we apply the random-walk algorithm Node2Vec+ with a set breadth-first search and depth-first search parameters. This gets us a creator vector representation. At last, we pick the top 5 creators from the True Affinity set (ranked set based on MCDM) for a consumer and use the creator embeddings to find the top 100 most similar creators from the entire creator. For a fast vector similarity search, we use the ScaNN algorithm. Stories Recommendation Our expected outcome is a list of storyid. Hence, from the 100 top affinity creators for each consumer (from the above approach), we pick the latest not watched story of each creator and add it to the recommendation pool of the consumer, stories ranked based on creator similarity score wrt. user’s true creator affinity. Conclusion This approach of Topsis MCDM and Node2Vec+ not only ranks creators for a consumer but also helped us to find similarities between creators of the same niche using a profile-visit co-occurrence graph. Reference PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest Billion Scale Recommendation at Taobao DeepWalk: Online Learning of Social Representations Ranking of entities with Multi-Criteria Decision Making Methods (MCDM) — Part One Ranking and Selection of the best with Multi-Criteria Decision Making (MCDM) — Part Two I hope you learned something new from this post. If you liked it, hit ❤️, subscribe, and share this blog with others. Want to discuss it further? Connect with me . here This newsletter is now read by more than 4500+ subscribers. If you are building an AI or a data product or service, you are invited to become a sponsor of one of the future newsletter issues. Feel free to reach out to shauryauppal97@gmail.com for more details on sponsorships. I am nominated for the HackerNoon 2022 Noonies, Vote for me: https://www.noonies.tech/2022/programming/2022-hackernoon-contributor-of-the-year-data Connect 1:1 Meeting here: https://topmate.io/shaurya I am open to Consults you can reach out to me on LinkedIn: https://www.linkedin.com/in/shaurya-uppal/ Data Science Book Recommendations: [1] The Book of Why [2] Naked Statistics

How to Build a User-Creator Affinity Model for a Short Video Platform

1Liner is an open-source library for getting alerts/notifications. It can be used in training jobs, data engineering pipelines, scheduled pipelines, in fact, any function’s return statement; with just 2 additional lines of code. KnockKnock I have been using it for quite some time and it has been amazing for me. I share my experience on LinkedIn as well. Why do I prefer 🚪✊? It helps me share error logs on my office comms channel slack or teams. It is easier for me to track now; instead of going to the cloud and scrolling through 100s of log lines and then finally knowing the error, directly getting logs on Slack is peaceful. It gives more visibility to the stakeholders. If someone in your team or PM asks you you would either go to your query engine or dashboard to get these numbers. Repeated requests like such make this process arduous and tiresome. Rather on each day schedule pipeline run with KnockKnock which metrics with these high-level metrics. Now everyone in the team has the visibility of a number of users to expect. “For how many daily active users do we have on the platform as of today?” You also get information on how much time a particular job run took along with Start Timestamp and End Timestamp. How should I integrate this? (Just 2 lines) from knockknock import teams_sender
WEBHOOK_URL = 'https://ABC.webhook.office.com/webhookb2/f762bb6b-cd10-4@894099-80c4-775a0cfe27a7/IncomingWebhook/3e3e4802df/45ec8631ef607'
@teams_sender(WEBHOOK_URL, user_mentions=['Shaurya Uppal'])
def func_name():
    return {'distinct user count': 1000, 'loss': 0.9, 'GCP write job': 'Complete'} Miscellaneous You can also set up these alerts on your Email, Desktop Notification, SMS, etc. using 🚪✊ in just 2 lines. Please feel free to share your views and feedback in the comments section below. Connect, Follow or Endorse me on LinkedIn if you found this read useful. To learn more about me visit: Here I also run a Newsletter “Data Science and Problem Solving” which is now read by more than 4500+ subscribers. If you are building an AI or a data product or service, you are invited to become a sponsor of one of the future newsletter issues. Feel free to reach out to shauryauppal97@gmail.com for more details on sponsorships. Other Recommended reads: [1] Experimentation when you can’t A/B Test | Beyond A/B Testing — Switchbacks & Synthetic Control Group [2] Mastering A/B Testing by understanding Pitfalls [3] Data Science in Ride-Hailing at Ola, Uber, Rapido, etc. [4] No more Cancellations? at Uber Subscribe to get Email Notification of every Newsletter being Published: HERE I am nominated for the HackerNoon 2022 Noonies, Vote for me: || https://www.noonies.tech/2022/programming/2022-hackernoon-contributor-of-the-year-data https://www.noonies.tech/2022/programming/2022-data-science-demon Connect 1:1 Meeting here: https://topmate.io/shaurya This article was first published here.

Setting Alerts With 2 Additional Lines

A cutting-edge data science model can only be created if the impact is measured properly. Pinterest upgraded everyone's preferred impact measurement metric, CTR, after carefully analyzing the problems. Every Data person adores the CTR measure as it is: Simple to explain to leadership, product managers do not question it, and measuring it is straightforward: Divide the number of clicks by the total number of impressions. CTR data is easily available, so it will be easy to calculate, track, and analyze. And, as a bonus, lots of advertisers want to have high CTRs too. So, you’ve aligned your advertiser and user interests! So, just throw a lot of advanced machine learning at the problem to predict and maximize CTRs and you’re done, right? Not so fast! CTR Suffers From Some Serious Shortcomings. “CTR Is Not the Key to User Engagement.” CTR just reports on clicks on a certain item, regardless of whether it is relevant or not. Some may claim that the user clicked because the content or ad was relevant, however, this is not always the case; it could be due to positional bias, clickbait, or other factors. These flaws will be discussed in further depth below. CTR is only useful for measuring short-term gains without consideration of user engagement. As a result, we require a metric to track user engagement. “You can fool all the people some of the time and some of the people all the time but you cannot fool all the people all the time” - Abraham Lincoln Problem 1 - Positional Bias Ads higher up on the page are more likely to be clicked, but this does not imply that the user finds them more valuable. If you fail to take this into account, you can conclude that users prefer the ads at the top of the page when, in reality, they are just more inclined to pay attention to them. Problem 2 - CTR Promotes Clickbait Items having a high click-through rate (CTR) may be "clickbaity" — they entice the user to click without providing anything helpful, instead sending them to ad-loaded slideshows that generate revenue for the advertiser but annoy the user. Problem 3 - CTR Ignores Other Signals CTR doesn't imply positive interaction. The person may still despise that specific piece of content. Hide Ads or drop-offs are powerful hate signals that CTR does not take into account. Above is a Google Ads example to capture explicit user dislike feedback. No point in showing a strong dislike signal ad with high CTR; it will eventually put the user off the platform someday. Problem 4 - Some Things Are Not Meant to Be Clicked Ads that do not require a click, such as a video, brand awareness, or pitch ads, such as an Independence Day discount at a nearby store or a short video of a new car launch. Okay..! We have now listed the problems with CTR but what is the Solution? Going Beyond CTR They adjusted their ads measurement metric to address the difficulties with CTR mentioned previously. A metric that incorporates engagement and more accurately captures user experience. User Metric: Let's discuss the Numerator first - Weighted Engagement on Ads Instead of just looking at CTR, we look at a weighted average of different actions — Clicks, Hides (Stop ads), Watch Time (on Video Ads), and Saves, among others — on the ads. Consider two ads: B is clicked more often than A, but it is hidden by a lot more people. One can readily conclude that ad type A is superior to ad type B because it receives more engagement and fewer users hide or disconnect with it while CTR alone shows the opposite picture. Conclusion - If we choose the actions and weights carefully, we can address concerns 2, 3, and 4 with CTR. Coming to the Denominator - Weighted Engagement on Neighbouring Organic Content To address Problem 1 (Position Bias) - We need to dig a bit deeper and understand that the CTRs drop on ads and organic content as users scroll through. One can verify this hypothesis of positional bias issue with A/B testing on the platform as well. LEARNING (This stands true in the majority of businesses) - If an ad or Feed Content on the 10th Position of the carousel gets a similar CTR to one on the 1st position, this means the ad on the 10th position is better. To normalize the effect of positional bias on ads content, we compare the average engagement rate on ads to that of the organic content in the spots before and after. This is where the denominator comes in. Experimenting with Above setup and New Ads Measuring Metric From the above table, we can infer that Treatment has lower CTR than Control but a lot better on our Weighted Engagement metric. Conclusion I understand that digesting and practically applying this new metric can be difficult because CTR has been used to gauge ads and content relevance for a long time, but change brings growth. If your ads monetization model is purely dependent on CTR, you can place a higher weight on Clicks when taking weighted engagement scores, but deleting engagement aspects and relying solely on CTR would make users unhappy in the long run. NOTE: Reference A better clickthrough rate: How Pinterest upgraded everyone’s favorite engagement metric by Philip Apps Is there anything you would like to add? Please feel free to share your views and feedback in the comments section below. Connect, Follow or Endorse me on if you found this read useful. To learn more about me: visit LinkedIn Here This newsletter is now read by more than 4500+ subscribers. If you are building an AI or a data product or service, you are invited to become a sponsor of one of the future newsletter issues. Feel free to reach out to shauryauppal97@gmail.com for more details on sponsorships. I am nominated for the HackerNoon 2022 Noonies, Vote for me: https://www.noonies.tech/2022/programming/2022-hackernoon-contributor-of-the-year-data Connect 1:1 Meeting here: https://topmate.io/shaurya Data Science Book Recommendations: [1] The Book of Why [2] Naked Statistics Also published here

Is the Pinterest Way to Measure Ads the Right Way?

Return to Origin (RTO) refers to the buyer’s product sent back/returned to the place of origin. RTO is one of the most significant issues of e-commerce companies which almost makes up for more than 30% of orders and every RTO constitutes a loss of INR 100–200 per order (shipping, manpower, packaging, fuel, time, etc.) Reasons for RTO To reduce or solve any issue one must understand the root cause of the problem first, from my experience in data science at e-commerce and in general following are the reasons for RTO: Incorrect Address entered by the user User Fraud — Evilness / Revenge 😈 User’s impulsive buying with COD options and later casually refusing to receive the delivery (in general studies claim that 40% of all the COD (Cash on delivery) orders are returned to the sellers) Item available at a lower cost on some other platform Business Impact by RTO Loss in (Fuel, Manpower, Packaging, etc.)forward and reverse logistics cost Blocked Inventory No doubt RTO cannot brought to zero but even slightest of reduction of RTO can save a lot for business. How can we reduce RTO? Identify non-deliverable addresses before shipping, improve success rates and increase revenue. Detect risky and fraudulent orders which pose a high risk to RTO and substantially cut down on RTO losses. Approach Incorrect Address Detection Most common is catching gibberish text in address input — (use of Markov Chain Model) Gibberish Detector The user's IP Location (Pincode/State) is not the same as the Input Address pin code and state. Some may raise the question that it could be a user placing an order for someone else; giving leverage to only old users but for new users beware as this could be a fraudulent case. Where is my IP Location? Another case; could be if you take the user’s geolocation/IP location — possibly a fraudulent user uses a VPN (use service ). VPN detector Identify the presence of the address component using NLP techniques: House Number, Area Name, City, State, and Pincode. (Pass an address only if the input address has all the components) Address Matching using Fuzzy Logic Post-identifying address components validate the correctness of Area Name, City, etc. from your past data collection (address store) or . map API A) Identify Fraud Registrations You may build your own model if you have lots of users on the platform. For startups, they can consider Amazon Fraud Detector which helps to catch fraud user registrations. Sample Input and Output B) Detect risky or fraud transactions by a User This is the most tricky and interesting data science core modeling part. Fraud problems are highly imbalanced problems and data crunch is a common issue. *: To identify if a transaction by a user is fraudulent or risky?* Objective To solve for this you don’t need lots of data points, we just need quality data points. Fraudulent users also follow some common traits we just need to catch those patterns. Features to consider: — Age, Gender, etc. Demographics — IP Location, GPS Location, Input Address, etc. Geographics — Device Info like Device Model, Device Brand, etc. Technographics — # of sessions, Avg. session duration on the app, account login and logout activity, items visited per session, # of RTO by a user in past, past orders reason for RTO, lifetime value of the user, etc. Behavioral Features — User email domain (legit or not), signup method, phone number, etc. Registration Information — Payment Mode (COD, UPI, Wallet, Credit Card, Debit Card, etc.), Checkout time, Transaction Amount, OTP auto-detected or manually entered, etc. Transactional Features Using these features, build a fraud prediction classifier model given this is an imbalance classification problem use class weights to treat it. Metrics: Use F-beta and AUC-ROC. Recall > Precision In the fraud example, the recall would be the percentage of fraudulent transactions we manage to detect, whilst precision is the percentage of the transactions we classify as fraudulent that are actually fraudulent. When classifying fraud, we mainly care about the recall of the fraudulent class, that is we want to correctly classify as many fraudulent transactions as possible. We still care about precision, just less than recall. Bonus Content: Benford’s Law Benford’s law is applicable mostly everywhere As a bonus for those who are curious, I wanted to touch on Benford’s law quickly. Benford’s law is this curve that appears all over maths. It appears in the Fibonacci sequence and the Collatz conjecture. If you take the first digit of the amount transacted by non-fraudulent transactions, you can see that the first digit's distribution (see below image) follows the curve in orange. If you try to do the same for fraudulent data, it simply does not follow Benford’s law as nicely. One could implement this into their model by for example applying some weighting to transactions by the number they start with for example. Conclusion Incorrect address identification, Fraud registration/signup detection, and Risky or fraud user identification — will help us know the probability/chances of RTO. Based on the severity of the risk, we can take the right measures like: Disabling COD for an order Banning a user from placing an order Changing return or cancellation policy for that user i.e. charging penalty on the user in case of frequent RTOs If you liked this blog, don’t forget to hit the ❤️ . Stay tuned for the next one! Connect, Follow, or Endorse me on if you found this read useful. To learn more about me visit: LinkedIn Here Email me if you have anything interesting for me: shauryauppal97@gmail.com Connect with 1:1 Book a Call here: https://topmate.io/shaurya I am open to Gigs or Consults you can reach out to me on LinkedIn: https://www.linkedin.com/in/shaurya-uppal/ I am nominated for the HackerNoon 2022 Noonies, Vote for me: https://www.noonies.tech/2022/programming/2022-hackernoon-contributor-of-the-year-data This story was first published here.

Using Data Science To Deal With RTOs

Do you know how physical or digital things acquire their prices? Or why a tomato doesn’t cost $1 million? Or why cryptocurrencies have certain values? The answer could be mostly summed up in the concepts of supply and demand that work inside and outside the crypto world. However, there are some crypto-specific things to consider as well. It’s, let’s say, the total quantity offered or produced. Conversely, demand represents the quantity of a good or service that consumers are willing and able to buy at a certain price. It’s the total quantity desired or bought. In economics, supply refers to the quantity of a good or service that producers are willing and able to offer for sale in a given market at a specific price and time. A seesaw or equilibrium + external factors There’s also a medium spot between , which we can call equilibrium. That’s the point at which the quantity supplied equals the quantity demanded in a market, resulting in a more-or-less stable price. You could picture a seesaw to understand this. supply and demand On the other hand, if there's too much of a product (increased supply) and fewer people want it, for any reason, prices tend to fall. The seesaw balances when supply and demand are equal, establishing a stable market price. An event that doesn't always happen. If more people want a product (increased demand) than is available, prices tend to rise. Other factors influence supply and demand, though. On the supply side, production costs, technological advancements, and government policies can significantly impact the quantity of goods or services available. Changes in input prices, such as raw materials or labor, can alter production costs, subsequently influencing supply. Additionally, shifts in technology can either streamline production, increase supply efficiency, or necessitate costly adjustments, affecting the overall supply. Consumer tastes and preferences can rapidly evolve, affecting demand for specific products. Changes in income levels can influence purchasing power, thereby impacting overall demand. On the demand side, consumer preferences, income levels, and external economic conditions play pivotal roles. Moreover, external factors like , geopolitical events, and natural disasters can disrupt both supply chains and consumer behaviors, creating fluctuations in supply and demand dynamics. The intricate interplay of these factors shapes the equilibrium in markets, determining prices and quantities exchanged. regulatory changes Some tomatoes As an additional example, let’s think about that tomato from the beginning. It has different prices in different countries, but never $1 million per unit. That’s because the world around 44.2 million metric tons of tomatoes every year. There’s demand for tomatoes, but also a lot of supply. If you’re in the countryside, you can even pick up some tomatoes for free. produces So, the tomato price ($0.0010 in Colombia, $0.27 in the USA, $1.5 in South Korea, etc.) is heavily determined by its intrinsic value or use (food), its supply, distribution costs, and likely local taxes. For one unit to cost $1 million it’d need to be very special in some way, like being the last-ever tomato on Earth (scarcity) and capable of curing cancer (additional intrinsic value). Now, if we created today a decentralized cryptocurrency called Tomatocoin (TMT), why or how would this one have any price? Why do cryptocurrencies have value? Usually, the interaction between supply and demand determines the market value of cryptocurrencies. The decentralized nature (without middlemen and regulations) of these digital assets means that market forces (people buying and selling) play a crucial role in establishing fair value, as opposed to centralized control seen in traditional financial systems. When demand outpaces supply, prices tend to rise, and vice versa. About the supply, contrary to most fiat currencies, crypto coins often have an intentional scarcity by design. That means they’re not limitless, but have a capped supply since the beginning. For instance, Bitcoin was designed to issue 21 million coins ever, nothing less and nothing more. Likewise, has a limited supply of 1 million GBYTEs. Our Tomatocoin could have a capped supply of 1 million TMT as well, but that’s only the beginning of acquiring a real price. We need demand, people desiring to buy it. GBYTE Then, why would people want to buy Tomatocoin? Maybe TMT is a pegged to the value of the tomato industry. Or perhaps is a representative share in a new investment model for tomato crops. Or it’s just funny, like Dogecoin (DOGE), and we can make a funny website to attract buyers. In this case, Tomatocoin would need to offer some added value to create demand. That depends entirely on what benefits or features the coin is offering. stablecoin a memecoin Bitcoin, on the other hand, is the first-ever cryptocurrency, considered secure and global. It has its value as a cross-border, pseudonymous payment method or speculative investment. is more decentralized, with smart contracts and cheaper and faster transactions. To sum it up, besides their limited supply, most cryptocurrencies need to provide some other service, benefit, or product to attract customers (demand) and raise their prices from zero. Obyte's offer More reasons for cryptocurrency demand In general, for the cryptocurrencies that already exist and provide some kind of product/service/benefit, the demand is influenced by several other factors. contribute to the rising demand worldwide. Additionally, speculative trading and the potential for high returns attract individual investors to the crypto market. Growing interest from institutional investors, increased adoption of online transactions, and a desire for financial liberty and privacy The heavy censorship and unstable economy in certain countries are also important adoption drivers. In nations where freedom of speech is restricted, and the flow of information and money is tightly controlled, cryptocurrencies offer a decentralized and censorship-resistant alternative. They provide a means for individuals to engage in financial transactions and access a global financial network without fear of interference. Besides, in countries facing economic volatility and hyperinflation, cryptocurrencies present a store of value and a hedge against currency devaluation. Citizens in these regions often experience difficulties in preserving their wealth due to the instability of local currencies. Cryptocurrencies like GBYTE, with their limited supply and decentralized nature, are perceived as a more stable and reliable store of value. Speaking of which, : all traditional currencies, stocks, and commodities (gold, oil, etc.) could be frozen or seized by third parties, for any reason —sometimes unjustified. Only cryptocurrencies provide you with full control of your own funds because only you have the keys. the decentralized nature of cryptocurrencies is, likely, their main advantage. No other asset worldwide possesses that characteristic The more decentralized the network is, the more protected you are. In Obyte, for instance, we eliminated miners as powerful middlemen. We only have less-powerful (OPs) to order transactions in a Directed Acyclic Graph (DAG) structure, where everyone can register their operations without permission or interference. In a world full of surveillance and control, the demand for decentralization and privacy is going higher globally! Order Providers Featured Vector Image by vectorjuice / Freepik

From Tomatoes to Tomatocoins: Exploring the Economics of Cryptocurrency Valuation

টমেটো থেকে টমেটোকয়েন পর্যন্ত: ক্রিপ্টোকারেন্সি মূল্যায়নের অর্থনীতি অন্বেষণ

Von Tomaten zu Tomatenmünzen: Erkundung der Ökonomie der Kryptowährungsbewertung

De los tomates a las tomatecoins: explorando la economía de la valoración de las criptomonedas

Des tomates aux tomates : explorer l’économie de la valorisation des crypto-monnaies

टमाटर से लेकर टमाटर के सिक्कों तक: क्रिप्टोकरेंसी मूल्यांकन के अर्थशास्त्र की खोज

トマトからトマトコインまで: 暗号通貨評価の経済学を探る

토마토에서 토마토코인까지: 암호화폐 가치 평가의 경제학 탐구

Dos tomates aos tomates: explorando a economia da avaliação das criptomoedas

От помидоров к томатокоинам: изучение экономики оценки криптовалюты

Domateslerden Domatescoinlere: Kripto Para Değerlemesinin Ekonomisini Keşfetmek

Từ cà chua đến cà chua: Khám phá tính kinh tế của việc định giá tiền điện tử

从西红柿到西红柿币：探索加密货币估值的经济学

From The Jetsons to Juicero: making the world a more comfortable place For the last decade, technology promised to ‘ ’. But is it delivering? Let's take a look at some products and services that received substantial media attention and funding since the early 2010s to find out: make the world a better place allowed users to send the word "Yo" to their contacts. Yo , then Stockwell, aimed to replace corner stores by installing vending machines in residential buildings (ideal for convenient toilet paper replacement. Those machines spared me some pretty desperate convenience store runs when I lived in San Francisco) Bodega sold a $400 juicer that was essentially a glorified bag-squeezer. Juicero developed a wearable device that claimed to induce relaxation or stimulation through electrical signals sent to the brain. Thync created a solar-powered inflatable lantern. LuminAID is a wearable device that delivers electric shocks to users when they engage in "bad habits" like biting their nails or oversleeping. Pavlok , then Spoonr, allowed users to find people in their area to cuddle with Cuddlr allowed people to rent out their bathrooms to strangers for a fee. Airpnp is a dating app that matches people based on their shared dislikes. Hater promised to create a bracelet that would project a touchscreen onto your skin, allowing you to control your phone without actually touching it. Cicret Amidst the deluge of tech discoveries and inventions promising utopia, the reality remains underwhelming. The majority of the list addresses niche needs. For many entrepreneurs, the tongue-in-cheek aim is to supply everything their college campuses (or mothers) no longer provide—an infantilized version of The Jetsons' future. When everything is dubbed "world-changing," what truly is? Clay Tarver, writer and producer of HBO's satirical "Silicon Valley," notes some big tech companies have forbidden employees from saying "We're making the world a better place." Tarver jests, "At the very least, we're making the world a better place by making these people stop saying they're making the world a better place." If you think the list of past flops might be biased, you may have a point. However, hindsight is always twenty-twenty. In reality, even recent tech trends are rife with startups suffering from a Napoleon Complex. Economist Robert J. Gordon contends that today's AI, which focuses on pattern recognition, isn't as groundbreaking as electricity or the internal combustion engine, despite what technologists and investors claim. Don't expect a world-shattering revolution to happen anytime soon. We're in a comfort crisis. The brightest minds in science and technology, from founders to influential investors, seem preoccupied with hype, external validation, and conformity. Prioritizing venture capital over sustainable businesses, chasing trends heedless of unique needs, and surrounding oneself with "yes" people rather than seeking critical feedback and diverse perspectives are all misguided endeavors. Countless talented individuals opt for safe, incremental ventures, such as SaaS companies and cookie-cutter consumer startups. Although these pursuits may yield some benefits, they ultimately neglect crucial human challenges, including existential ones. This not only stifles progress but also represents a missed opportunity for gifted individuals to leave a lasting impact on the world. In essence, for any exceptionally capable person, shirking a noble quest isn't just neutral—it's a net negative for society. Velocity is everything A worthy endeavor should target a problem substantial enough to rightfully claim social impact credentials. Tough challenges with a low chance of success, yet a social promise enticing enough to overlook operational complexities—think artificial general intelligence (AGI), curing Alzheimer's disease, sustainable nuclear fusion, or even building a . network state However, just because the ‘mission’ is ‘impossible’, it doesn’t mean it’s worth pursuing – like “ ”, whatever all of that means. The difficulty axis is just one piece of the puzzle and a somewhat one-dimensional perspective on problems. Hard missions without an angle are an exercise of style at best, like the , and pure fugazi at worst, like Theranos. shaping the future of web3 through storytelling, experiences, and community war on cancer The need for ambitious endeavors to improve the world is driven by technological velocity, which is often measured by the degree of operating leverage - the ratio of fixed costs to variable costs, that explains how much of a company’s operating income changes in response to a change in revenues. Unit economics tells the true story of broad adoption. It’s either that or knowing a special secret, a glimpse of truth at a technological level that can flip the game on its head. Moonshot builders can be grouped into two types: those with in-depth knowledge and understanding of technology, research, and fieldwork, and those who excel in execution and operations. The first group discovers bold breakthroughs, while the second accelerates development and ensures they become a reality. Both roles are critical in meeting the needs of society, and it's important to recognize one's unique strengths within this framework. Your author is an action-taker. As a non-scientific, non-technical expert, I have very little to say about the process of scientific insight generation. But I am interested in both very large and very small patterns – how markets and human behavior change, as well as how the unit economics of a product, at its most atom-like level, can narrate the story of technological velocity. When it comes to resource allocation – whether that is money, time, or energy – velocity is everything. Three case studies Exceptional quests are those that address significant societal issues while showcasing remarkable velocity. While assessing a quest's potential impact might be simple, understanding velocity is more complex. Velocity represents a learning factor, indicating the pace of change. It serves as a proxy for decreasing adoption costs, which are crucial for turning breakthrough technologies into widely adopted products. The relationship between experience – measured as the cumulative installed capacity of the technology – and the price of that technology is called the learning curve of that technology. The relative price decline associated with each doubling of experience is the learning rate of a technology. That more production leads to falling prices are not surprising – such ‘economies of scale’ are found in many corners of manufacturing. If you are already making dinner, it isn’t that much extra work to accommodate an extra guest. For over six decades, the concept of technological learning has suggested that a technology's performance enhances as experience accumulates. I argue that the best way to anticipate all future technological advancements is to concentrate on paradigms that exhibit learning curves. There’s no shortage of examples of innovations that benefitted from technological learning curves. LED lighting has emerged as an affordable, energy-efficient alternative to traditional bulbs. Electric vehicles now offer a price-competitive substitute for gasoline cars. To get our expectations for the future right we ought to pay a lot of attention to those technologies that follow learning curves. Initially, we might only find them on a high-tech satellite out in space, but the future belongs to them. Most technologies obviously do not follow a learning curve – the prices of bicycles, fridges, or coal power plants do not decline exponentially as we produce more of them. But those which do – like computers, solar PV, and batteries – are the ones to look out for. They might initially only be found in very niche applications, but a few decades later they are everywhere. To get our expectations about the future right, I argue that today there are three important areas where upstream technological paradigms are exhibiting learning curves in their unit economics – semiconductors, cancer detection, and solar batteries. Moore’s Law and Semiconductors In the 1960s, Intel co-founder Gordon Moore observed that the number of transistors in an integrated circuit doubled at a clock-like pace, leading to greater processing power at the same cost. To Moore, there was no reason that progress couldn’t continue. And there hasn’t been. As semiconductors became cheaper and more efficient, they penetrated increasingly more aspects of our daily lives. By and large, they continued to drive improvements in things we already use, from phones to cars to appliances. And of course, there’s , which you’d have to be living under a rock at this point to not have heard of. Ask it anything and it’ll very confidently tell you… Well, something. chatGPT Morris Chang, known as the Godfather of Taiwan's chip industry, developed this concept into a revolutionary pricing model called 'learning curve pricing' or 'experience curve pricing,' which involved setting chip prices below initial costs to optimize production volume. As a result, he captured market share, enabling production lines to run at maximum capacity and reduce time to increase yields. While superlatives cannot adequately describe the profound effects of the semiconductor industry on human progress, the expanding use of semiconductors is a clear sign of the fundamental importance of this industry that stood the test of time. The minuscule transistorized computer chips made from semiconductors have become the lifeblood of our 21st-century information society, just as oil was to the industrial world of the 20th century. Without the ubiquitous electronic infrastructure powered by computer chips today, virtually nothing would run. Chip manufacturing may be the most complex, process-controlled industry the world has ever seen. Moore’s Law accurately charted the pathway in size reduction and thus in performance for those transistors. Investors, producers, system manufacturers, and consumers alike all benefited from the Swiss watch-like regularity of the computer chip product cycles. Unlike no other business in the world, the semiconductor industry had an accurate, proven roadmap for its future…with clear technological velocity, orchestrated to the rhythm of Moore’s Law. Flatley’s Law and early cancer detection Cancer continues to be a pressing public health issue, with mortality rates decreasing by merely 19% since 1990, compared to a decline of over 50% for cardiovascular diseases. The COVID-19 pandemic has further exacerbated the urgency to address cancer due to missed screenings and diagnoses affecting millions. Nonetheless, advancements in diagnostic and therapeutic innovations offer the potential to significantly reduce cancer mortality rates. Early detection is paramount, as cancer is a progressive disease, and advanced tumors account for a disproportionate number of deaths. Molecular testing is essential for precision therapy because it identifies tumor-specific mutations that guide oncologists in selecting the most effective treatments. Approximately two decades ago, when the human genome was first sequenced, it took a team of researchers at the National Institutes of Health (NIH) nearly 15 years and over $2.7 billion to accomplish the task. Today, a human genome can be sequenced in roughly a day for around $500, and the cost is anticipated to soon drop to $100. Flatley's Law (named after former Illumina’s CEO), an analog to Moore's, demonstrates even faster advances and more significant cost reductions. Flatley's Law highlights the declining cost of cancer detection tests, such as multi-cancer earlier detection (MCED) tests, which can identify multiple cancer types from a single blood draw. These tests have the potential to decrease cancer mortality rates by 15% at a $500 reimbursement price, marking the real turning point for widespread adoption. Reimbursable tests are the Schelling point of cancer detection. With falling prices and continuous improvement in accuracy, multi-cancer earlier detection (MCED) can become a part of a routine check-up for most and avert an unprecedented number of cancer-related deaths. Wright’s Law and solar batteries To reduce emissions the world needs to rapidly transition towards a low-carbon energy system. Around three-quarters of come from energy and industry. One of the barriers to this energy transition has been the relative cost of different energy sources. Fossil fuels were cheaper than renewables and therefore became the dominating sources of energy. global greenhouse gas emissions Thankfully this is changing quickly. The cost of renewable technologies has plummeted – they’re now cost-competitive or cheaper than new fossil fuels. In 2009, it was more than three times as expensive as coal. Now the script has flipped and a new solar plant is almost three times cheaper than a new coal one. The price of electricity from solar . declined by 89% between 2009 and 2019 But the cost of electricity technologies themselves is only part of what matters for this transition. One of the challenges that renewables face is that they produce energy intermittently. The sun doesn’t always shine and the wind doesn’t always blow and so we don’t get a steady flow of generation throughout the day. An obvious solution is to store excess energy and then release it later. But to do so, we need lots of energy storage and this adds large costs to our energy system. The price of lithium-ion battery cells . A battery with a capacity of one kilowatt-hour that cost $7500 in 1991, was just $181 in 2018. That’s 41 times less. What’s promising is that prices are still falling steeply: the cost halved between 2014 and 2018. A halving in only four years. declined by 97% in the last three decades To put this in perspective: the popular electric car – which is also one of the most affordable models – has a 40 kWh battery. At our 2018 price, the battery costs around $7,300. Imagine trying to buy the same model in 1991: the battery alone would cost $300,000. Nissan Leaf What this means is that batteries have been getting smaller and lighter for any given electrical capacity. You might have noticed this yourself as your mobile phones were getting lighter and slimmer. This is a crucial technological improvement as one of the major drawbacks of some battery technologies is that they are heavy and this limits their use in a number of technologies that are still fossil fuel powered. Making innovation irresistible The history of technology is shaped by groundbreaking innovation reaching widespread adoption. But the vast majority of “world-changing” ventures functionally amount to economic noise. To achieve double-digit GDP, cure cancer and halt environmental decay we need a bold tech with straightforward economics. Bull markets, abundant venture capital funding, and the Zuckerberg-esque mythology of the rebel founder muddled the waters of the second half of this formula - celebrating outsized ambition not only as a necessary but also a sufficient condition. This is totally backwards. Moreover, one of the very worst misconceptions about technological paradigms is that they happen overnight. They don’t. Societal technological revolutions are exceedingly rare and the implementation challenges are much larger than the technical considerations alone. The chronicle of technological progress is interspersed with a history of setbacks. This is attributable to diseconomies of scale and the inability to achieve the necessary economic curves that lower prices and promote adoption. Nuclear power serves as a prime illustration of this phenomenon. In many places, building a power plant has become more expensive over time. This is of course very unfortunate since nuclear is both a low-carbon source of electricity and one of the safest sources of electricity as we have seen. One reason for rising prices is the increased regulation of nuclear power. A second reason is that the world has not built many nuclear power plants in recent years so supply chains are small, uncompetitive, and are not benefiting from economies of scale. Learning curves, after all, mean transferring the knowledge gathered in one instance to another. No repetition, no learning. Reducing the costs of innovative technological paradigms is crucial for achieving widespread adoption. The technical advantages of MCEDs compared to traditional biopsies or renewable energy over fossil fuels remain largely insignificant to society unless these innovations can achieve cost parity with their alternatives. The key indicators of progress lie in unit economics. If you are actively involved in, investing in, or simply passionate about the technology industry, I encourage you to take a step back and carefully evaluate your work at a 10-minute grocery delivery startup or a tool designed for Web3 communities.  Have a hard, critical look at their mission and consider whether you are building " " that just… squeezes giant ketchup sachets of fruit. an incredibly complicated piece of engineering Also published . here

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