Comparative Visualization of IBM&Google

I want to create an infographic using data provided (related to Google and IBM). To create a comparative visualization, enabling the reader to have an experience with this dataset. When developing an infographic, let the data flush out the concept rather then work up a concept and try to force the chart into an idea… Continue reading Comparative Visualization of IBM&Google

Features Selection in Python

We talked about features selection based on Lasso(https://charleshsliao.wordpress.com/2017/04/11/regularization-in-neural-network-with-mnist-and-deepnet-of-r/), and autoencoder. More features will make the model more complex. it can be a good idea to reduce the number of features to only the most useful ones, and discard the rest. There are three basic strategies: Univariate statistics, model-based selection and iterative selection. We use the… Continue reading Features Selection in Python

Clustering Application in Face Recognition in Python

We used face datasets for PCA application here: https://charleshsliao.wordpress.com/2017/05/28/preprocess-pca-application-in-python/ It also will be interesting to see how clustering algorithms assign images into different clusters and visualize them. We use the data from sklearn library(need to download face datasets separately), and the IDE is sublime text3. Most of the code comes from the book: https://www.goodreads.com/book/show/32439431-introduction-to-machine-learning-with-python?from_search=true

Clustering Algorithms Evaluation in Python

Sometimes we conduct clustering to match the clusters with the true labels of the dataset. Apparently this is one method to evaluate clustering results. We can also use other methods to complete the task with or without ground truth of the data. We use the data from sklearn library, and the IDE is sublime text3.… Continue reading Clustering Algorithms Evaluation in Python

DBSCAN in Python

Another very useful clustering algorithm is DBSCAN (which stands for “Density- based spatial clustering of applications with noise”). The main benefits of DBSCAN are that ###a) it does not require the user to set the number of clusters a priori, ###b) it can capture clusters of complex shapes, and ###c) it can identify point that… Continue reading DBSCAN in Python

(Ugly)UX test for UN APP of Women Time Usage

https://invis.io/TSANOP4J4 clickable prototype here. During this user usability test, I have three users test the application developed for Polly and Sam mainly. Most of them are confused at very first on the tile page. After they read the instruction about how the tiles work they seemed to like the idea. They also ask questions about… Continue reading (Ugly)UX test for UN APP of Women Time Usage

Tips for Presentation

A great web for presentation: http://www.garrreynolds.com/preso-tips/deliver/ At all times: courteous, gracious, & professional When audience members ask questions or give comments, you should be gracious and thank them for their input. Even if someone is being difficult, you must keep to the high ground and at all times be a gentleman or lady and courteously… Continue reading Tips for Presentation

Four truths of the story teller&Four principles for Chart Visualization

Four principles are by no means exhaustive: •Simpler is better. •More is better. •Different is better. •Creativity is better. For truths of the story teller: it must be true to the teller, embodying his or her deepest values and conveying them with candor; true to the audience, delivering on the promise that it will be… Continue reading Four truths of the story teller&Four principles for Chart Visualization