Machine Learning adoption is widespread across all industries. Its ability to perform specific tasks better than humans creates tremendous opportunity to improve economic productivity. The global machine learning market is projected to attain a Compounding Annual Growth Rate (CAGR) of 43% between 2020 and 2024, growing from $7.3b to $30.6b. As practitioners of ML applications, we are all incentivized to focus on the benefits of this technology in order to procure investment and resources.

However, I think we need to pause and introspect on it’s shortcomings just as much as we tout its benefits. Recognizing that Machine Learning can impact…


Logistic Regression is an algorithm that predicts the probability an observation belongs to one of two classes. If the observation being predicted is an event, the binary dependent variable is encoded as a 1 if the event is likely to occur, or as a 0 if it is not likely.

In this example, I implement a 5-step logistic regression to predict whether a reservation is likely to be cancelled so venues are prepared to find new bookings for empty space.

The dataset used is on a travel business and contains 4238 observations. Its categorical variables include destination country, property type…


I used the VGG-19 Convolutional Neural Network to sift through photos from my nature photography collection and detect images containing trees. It was a fun exercise where I learned a lot about how CNN’s work under the hood. Below I describe the CNN architecture, process and inputs and provide an 8 step implementation of how I structured mine.

CNN Architecture, Process & Inputs

Architecture:

CNNs contain a combination of layers which transform an image into output the model can understand.

Melisa Bardhi

Bridging people to insights with data.

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