A crypto token equal to One Kilogram of Hydrogen gas!! What’s that?
I had heard of so many Crypto COINS related to a blockchain product or service, but how’s it related to Hydrogen?
TL;DR — launch the Telegram link to get FREE airdrop2 tokens
Read further to know more about WPP energy and the token sale.
WPP ENERGY, a decade old Swiss Company, was established in 2009. It has established itself as a repository for disruptive energy and environmental technologies. WPP has received 25 year global exclusive licenses representing patented disruptive technologies.
WPP has several projects in conversion of water…
Oh, there’s another crypto. Should I wait or jump to catch it?
Will it zoom or plunge? What is a chain after all. I had heard of Crypto COINS, but what’s a chain?
Polkadot is a next-generation blockchain protocol connecting an entire network of blockchains to operate seamlessly together at scale. Polkadot allows any type of data to be sent between any type of blockchain.
You can use the best features from multiple specialized blockchains. This way, Polkadot helps new decentralized marketplaces to emerge, offering fairer ways to access services through a variety of apps and providers.
While blockchains have…
Deciding between Robo advisor and mutual fund can be tricky. In this article we will briefly check if Robo advisors are better than Mutual Funds. We will also look at Robo advisor pros and cons.
Both Robo advisor and Mutual Funds can be extremely beneficial for short and long term investments and for generating regular income. However, the question we seek to answer in this article is whether one option is significantly superior to the other.
Let’s start with taking a quick look at what is a Robo Advisor.
A Robo-advisor is a reliable digital financial service that utilizes technology…
Backward elimination is an advanced technique for feature selection to select optimal number of features. Sometimes using all features can cause slowness or other performance issues in your machine learning model.
If your model has several features, it is possible that not all features are equally important. Some features actually can be derived from other features. So o improve performance or accuracy, you can ignore a few features.
Is there any vehicle other than direct stock investment which is safer and helps you be well-diversified? How to get a list of all possible permutations and combinations of all types of investments and how to rank them in order of your preference: 1 or 2 or 3 year return; volatility (market swings); risk of loss; industry or sector or geographical diversification; and last but not the least, active management by an astute fund manager or just blindly following a stock market index?
This is the part II post in the series of posts on how I came up with…
FOMO (fear of missing out) in current times, when US and global stock markets are soaring, tops the list of emotions generally seen in any investor. Should I invest right now? If I don’t invest, I will loose those thousands or millions I can earn like my colleague earned in just last 6 months? How could I miss buying Tesla stock when all my friends / colleagues bought it and now have almost $100k profit in their account? I missed buying Zoom stock and yesterday I read that Zoom’s CEO and Li Ka Shing have become billionaires just with that…
This project uses UCI dataset of almost 400 cars with accurate values of following parameters.
1. mpg: continuous
2. cylinders: multi-valued discrete
3. displacement: continuous
4. horsepower: continuous
5. weight: continuous
6. acceleration: continuous
7. model year: multi-valued discrete
8. origin: multi-valued discrete
9. car name: string (unique for each instance)
The idea is to train a machine learning model to learn the relationship (weights for regression equation) between dependent variable (y) and independent variables or features (x1, x2, x3 etc).
It’s obvious that the mileage of a vehicle doesn’t depend purely on only these parameters. There are several other…
Continuation story of Diamond Price Prediction using ML
This post is more of a continuation of the Diamond Price Prediction using Machine Learning story.
The first post sows the seed for Machine Learning regression algorithms to predict the price of a high value item using publicly available database of roughly 54000 diamonds on Kaggle.
This particular post talks about downloading data from PriceScope and CaratLane and using it to predict diamond prices
I have downloaded the data for roughly 1500 diamonds for my own learning and not for any commercial purpose. The data is available on public domain on pricescope.com…
Using PriceScope and CaratLane Diamond listing
Amongst a variety of items that are not quantitatively or statistically valued by buyers, Diamonds are possibly the most valuable. The purchase is far less from rational with a heavy bent on emotional ties. The jewelers would entice every man (and woman) by marketing it as a necessity for the occasion and as a status symbol, and by calling this pricey and unaffordable item as priceless.
The actual value of a diamond however is determined by a gemologist after inspecting its various “features” (let’s start using the proper machine learning words now since this…
Following are the notes I created before my Machine learning / NLP interview.
This is a set of really concise notes with brief explanation, quick answers and python code with comments for a lot of Machine learning and NLP algorithms using ML libraries like Sklearn, Spacy, NLTK, Numpy, Pandas, Matplotlib.
I have split the notes into multiple stories. This one is on some short bullet-point notes. The others are on NLP; Neural networks; NER using Spacy; Prodigy
p.s. all the parts combined is a really long post. …