Categorical Correlation with Graphs: In Simple terms, Correlation is a measure of how two variables move together. For example, In the real world, Income and Spend are positively correlated. If one increases the other also increases. Academic Performance and Video Games Usage is negatively correlated. Increase in one predicts a decrease … Read more3 Awesome Visualization Techniques for every dataset
Getting to know the data We use the pandas library for this, and here is what one of files found in Open Data looks like: Sample of the 1999 Freedom of Information Request File We have 18 files, one for each year, 1999 to 2016, 576 requests in total, and amazingly all … Read moreWhen Data is Scarce… Ways to Extract Valuable Insights
When it comes to data preparation and getting acquainted with data, the one step we normally skip is the data visualization. While a part of it could be attributed to the lack of good visualization tools for the platforms we use, most of us also get lazy at times. For … Read moreCreate basic graph visualizations with SeaBorn
Train model for 5 cycles with learning rate = 1e-2 We’ll train our model for 5 epochs (5 cycles through all our data) using the fit_one_cycle function. Training and validation losses Notice the metrics getting displayed i.e training_loss and valid_loss? We use them to monitor model improvements over time. Our … Read moreHow a team of deep learning newbies came 3rd place in a kaggle contest
Considerations before clustering Clustering is one of the most widely used forms of unsupervised learning. It’s a great tool for making sense of unlabeled data and for grouping data into similar groups. A powerful clustering algorithm can decipher structure and patterns in a data set that are not apparent to … Read moreWhen Clustering Doesn’t Make Sense
Attempt #2 — Linear Discriminant Analysis (LDA) Figure 4 — Real dataset (left), LDA fitted dataset (right) Linear Discriminant Analysis (LDA) is an immediate improvement from our first attempt. Figure 4 shows the output from the LDA model on our training set. We no longer exhibit masking and the number of misclassifications have greatly reduced. … Read moreClassification: A Linear Approach (Part 1)
(Source = https://www.datanami.com/2018/05/30/opening-up-black-boxes-with-explainable-ai/) Introduction Breakthroughs in Artificial Intelligence (AI) taken place during the last few years enabled possibilities for computers to perform tasks that would have been impossible to do using traditional software programming. These advancements are now opening up to us an entirely new world of potential applications for … Read moreNeed for Explainability in AI and Robotics
The head of Kaggle Learn gave me a flick between the eyes with his webinar Have you ever had a moment in your career when you think you’re on the right path, when you’re following a steady plan that leads you right to where you want to be and then have … Read moreBecoming a Data Scientist — When Dan Becker pointed out I had it all wrong
A Data Analysis of the 2020 Presidential Contenders In the 2016 primaries, one key to Donald Trump’s success was his ability to get media attention. By some estimates, the obsessive wall-to-wall coverage of Trump was the equivalent of 2 billion dollars of free advertising¹ for his campaign. In this age … Read moreWhich Democratic Candidate Gets the Most News Coverage?
Build it, they will come Building a Data Lake should not be an objective in itself, but should rather be a means to an end; the end being to address digital transformation and data driven initiatives in a company. Yet many IT departments started building Data Lakes because it’s cool and … Read moreData Lake: an asset or a liability?