Part 1: Building a stock market report web app using streamlit library
Have you ever wanted to build a data-driven web application for your data science projects and were intimidated by the difficulty of coding in Django or in Flask?
If you have faced such difficulties then I would suggest you to just follow this article because I’m gonna show you how to build a stock price web application in just a few lines of code using Streamlit library and deploy it in AWS free tier EC2 instance.
We will divide this process into two parts: Building & Deploying
By using Lazy Predict library we can compare the best performing classification and regression algorithms effortlessly. This library was authored by “Shankar Rao Pandala” and here is the link to the documentation.
We are going to use the breast cancer dataset for comparing classification algorithms and Boston housing dataset for comparing the regression algorithms with just few lines of code for each task using this library.
Installing the library:
!pip install lazypredict
Use the above command in order to install the lazypredict library
Importing necessary libraries and breast cancer data:
# Import libraries
from lazypredict.Supervised import LazyClassifier
from sklearn.datasets import load_breast_cancer
In my previous article, I have discussed about how A.I was originated and how it is becoming creative. If you liked it you’re going to be fascinated with this one. We’ll be diving into some very interesting stuff. In December of 2017 a group of researchers asked themselves will A.I be writing most of the computer code out there? And they estimated that this could happen by 2040 and the thing is we may be already seeing the start of it. We know that AutoML by Google is an A.I …
Problems posed by the computer are really no different than the problems we have with other products of technology. It’s gonna take a great deal of wisdom on our part to manage them. But if we do, we’re going to make a much better world.
Artificial intelligence or AI has the potential to revolutionize our world the way we do things and how we live and you can say that it’s already starting to do that AI will be one of those big tools that propels us into a new future like computers and the Internet did decades ago.
While working on classification problems, we often confuse about which metric to use that perfectly depicts the performance of our model especially when there are imbalances present in our dataset. Focusing on one metric over the other will help in better understanding of our data. Under the hood these are simple parameters which just need a little demystification.
When you observe the name “Receiver Operating Characteristic Curve (ROC)”, it may seems a little odd since it is quite uncommon in statistical background. Yes your guess is correct, ROC is originally used during the world war scenarios by the operators working…
If you are here I assume that you have already come across the concept of confusion matrix and are looking to know more about it. I have shared some tricks in the article. I will try to explain the concept as a story so that it will last longer in your memory. In the end I have made an interactive game for a quick recap of the concept.
Confusion matrix is used to predict the performance of a model. Imagine this confusion matrix as a great reviewer of models. He explain’s the ways in which a model got confused while…
Tech Lead@Sargaa | ML Practitioner | Game Developer