Sentiment Analysis of Twitter’s US Airlines Data using KNN Classification
- We will build a k-nearest neighbors classifier from scratch, evaluate and verify our model’s performance results by comparing it with Scikit-learn’s built-in classifier and, finally, enhance our model’s performance by tinkering with the input features.
- Before we slide into the implementation of k-nearest neighbors algorithm for this particular problem, it is imperative to note that the aforementioned preprocessing and feature extraction needs to be done on the test dataset, too.
- The way that the classification algorithm will work is that for a given tweet in the test dataset (d), we will compute Euclidean distance between d and every sample in the training dataset (D).
- In order to verify our results in the implementation process penned down above, we use Scikit-learn’s kNN implementation to train and test the k nearest neighbor classifier on the provided dataset and compare its performance measures with the ones we got above.
Spotify launches '2020 Wrapped' with new features including quizzes, badges and, yes, stories
- Spotify is today launching its 2020 Wrapped personalized experience — the company’s popular year-end review of users’ favorite artists, songs, genres, and podcasts.
- And it will include new features, like in-app quizzes, a “Story of Your 2020” dedicated to users’ top song of the year, new Wrapped badges, personalized playlists, customization options for social sharing, and other additions.
- But non-users will get a taste of Wrapped with a web version, where Spotify will share its broader global listening trends, including the most-streamed artist, top three podcasts, and other popular music insights.
- And for listeners in the U.S., U.K., Ireland, Australia, New Zealand, and Canada, a third playlist called On Record will introduce a mixed media experience that highlights users’ top 2020 artists.
Human Activity Classification on the selfBACK Data Set with pycaret and…
- I wanted to create a supervised learning problem, which means that I needed to use “windows” of data (slices of time, e.g. 1 second or 1/100th of a second long) which would serve as the features to train on, while an activity (e.g.
- The sensors they used are running at 100 Mhz, but you might need or want to have more than 1/100th of a second as a snapshot of data to determine what kind of activity a person is doing.
- So let’s say you chop up the data set into windows of 100 points (1 second long snapshots).
- The Python deep learning library keras has a TimeSeriesGenerator functionality which can create such feature/target data pairs from time series data so I had to cook up some code for creating windows.
- I didn’t want to train it for a long time, so I just tried 1 model.
Google Maps community feed will highlight changes in your city
- Google is introducing a new community feed to Maps that the company says will help keep you informed of all the latest developments in your city, including new restaurant openings and service changes.
- Located in the app’s Explore tab, the feature collects all the latest reviews, photos and posts submitted to Maps by local experts, as well as people you know and merchants.
- Google doesn’t explicitly mention the coronavirus pandemic, but it’s easy to see how a feature like this was likely informed, at least in part, by recent events.
- In a place like New York, a recent report highlighted by The New York Times found that by the end of the pandemic, approximately one-third of the city’s small businesses could close permanently.
- The new community feed in Google Maps is now rolling out to all Android and iOS devices globally.
Microsoft Teams is getting CarPlay support for calls
- Microsoft has revealed a number of features it’s bringing to Teams calls.
- It will soon offer CarPlay support, so you can place and answer Teams calls using Siri while you're on the road.
- Other features include an option to merge calls, enhanced reverse number lookup (i.e. caller display) and spam call detection.
- Teams admins can now change the default storage location for call recordings to OneDrive and SharePoint instead of Microsoft Stream, which could make it easier to share recordings or transcripts.
- Another useful feature is on the way early next year: you'll be able to transfer calls between your phone and your computer.
- That, along with the CarPlay support, will come in handy when you need to move to another location while you're on a call.
- A low-data mode will also arrive in early 2021.
AWS launches SageMaker Data Wrangler, a new data preparation service for machine learning
- AWS launched a new service today, Amazon SageMaker Data Wrangler, that makes it easier for data scientists to prepare their data for machine learning training.
- In addition, the company is also launching SageMaker Feature Store, available in the SageMaker Studio, a new service that makes it easier to name, organize, find and share machine learning features.
- AWS is also launching Sagemaker Pipelines, a new service that’s integrated with the rest of the platform and that provides a CI/CD service for machine learning to create and automate workflows, as well as an audit trail for model components like training data and configurations.
- As AWS CEO Andy Jassy pointed out in his keynote at the company’s re:Invent conference, data preparation remains a major challenge in the machine learning space.
- Data Wrangler comes with over 300 pre-configured data transformation built-in, that help users convert column types or impute missing data with mean or median values.
A Hierarchical Clustering of Currencies
- In this case study, I use clustering to understand how various currencies might be grouped in terms of their behavior to global financial market factors.
- Moreover, partitioning algorithms such as K-means or Affinity Propagation work best with hyperspherical clusters that occupy roughly similar-sized segments of the dimensional feature space, which is likely not the case with currencies.
- A key consideration in the context of macro financial data such as the currency market indeed is that the clusters in the feature space might be of varying densities and radii.
- Moreover, DBSCAN likely won’t work optimally with the sample size in hand because the algorithm requires a minimum number of objects within a defined neighborhood to be considered a cluster, and the minimum needs to be at least equal to the number of features plus one.
Why & How to use the Naive Bayes algorithms in a regulated industry with sklearn | Python + code
- In a short answer, because Naive Bayes is simple, extremely fast, provides good results, is easy to implement in an IT production, is well suited when the training set does not fit in memory and is easy to explain in a regulatory industry.
- Despite its naive assumption it provides good results for classification because if we consider a loss function such as the zero-one loss, it does not penalize an inaccurate probability estimation as long as the maximum a posteriori’s probability is assigned to the correct class.
- As a result the posterior probabilities for “buy more than $X” (ctgrclPbuymore) & “buy less than $X” (ctgrclPbuyless) for the observation 0 of two_obs_test are respectively 0.9886380451862543 and 0.01136195481374564.
- For the denominator, Ny represents the overall sum of all the values in the database for the class y + alpha to be multiplied by n which represents the number of features used (without the target).
Churn Analysis Using Information Value and Weight of Evidence
- Let’s focus on running attribute relevance analysis on Telco dataset to understand customer churn.
- At the same time, some of the variables (like contract, tenure and internet service) suggest that the relation between values of those features and churn is very strong — so strong that it should be examined carefully.
- It’s interesting to see that customers using Fiber optic are much more likely to churn — it may suggest some problems with the service.
- In order to understand this relation it’s good to check which payment methods are recurring and what are the possible issues that customers have while using electronic check.
- Analysis of features with both IV & WOE and Chi-square test & Cramers’V shows some interesting relations between results of those two methods.
Daily Crunch: Facebook acquires Kustomer for $1B
- Facebook makes a billion-dollar acquisition, we learn more about Twitter’s Clubhouse-style feature and Moderna applies for emergency authorization for its COVID-19 vaccine.
- So with this acquisition, Facebook can improve its offerings for businesses that have a presence (in some cases, their primary digital presence) on the social network.
- Facebook isn’t the only social media company making acquisitions to improve its customer service features.
- Alphabet’s DeepMind achieves historic new milestone in AI-based protein structure prediction — The advance in DeepMind’s AlphaFold capabilities could lead to a significant leap forward in areas like our understanding of disease, as well as future drug discovery and development.
- Curio Wellness launches $30M fund to help women and minorities own a cannabis dispensary — The new fund, started by the Maryland-based medical cannabis company Curio Wellness, aims to help underserved entrepreneurs entering the cannabis market.