Niranjan Ganesan
I consider myself fortunate to have been born in the 90s, the era which connects “everything but tech” with “everything tech”. To be sure my brother and I did not miss the dotcom wave, my parents invested in a personal computer at the start of the millennium. Little did I know that this would shape my career, two decades later.
As a teen, my tech career had officially begun when I saw my grandparents recording their monthly expenses in a notebook and I developed an expensing app called “Cash +” to address it. I had it on a floppy disk (the most accessible tech at the time) to give their user experience (UX) flexibility when they travel. It was easier to carry the disk than the physical notebook.
Once I realized I had the acumen for tech, I decided to pursue engineering and then an MBA in highly ranked universities. My challenging MBA program took me to 3 culturally diverse cities each semester - Singapore, Sydney & Dubai where I had to learn to be frugal, fluid, and adaptable and all these qualities helped my career post MBA.
At graduation, I set a goal for myself - to explore different management functions to select the path in tech that I would thrive in. Looking back at my career experimentation, I’m glad that I was able to build & and launch products at Jio, take products & and services to the market through strategic B2B partnerships at Redington, and increase brand awareness through online marketing at Zero&One.
Last year, I decided to segue into a more technical path in Data Science and Machine Learning. I’m currently building my network in the US and proactively connecting with tech leaders here in the Data Science and Machine Learning fields.
Sessions
The goal of this project is to predict the box office outcome of an upcoming movie based on a wide variety of feature set like poster (images), revenue, language, title, genre, and popularity score. This project aims to help a content creator to gauge how their trailers are going to be received by audiences, based on the historic data, so that can gauge their work and improve as needed before publishing their work. Using a simple convolutional neural network (CNN) with one convolutional layer and three fully connected layers to extract visual features from the posters, the model was trained using MSE loss function with Adam Optimizer. Early stopping was implemented to prevent overfitting. The model was able to predict the outcome of a movie with an accuracy of 89% based on features extracted from posters alone. In addition to still image posters, trailers convey the idea of the movie. The tonality of trailers depend on the visuals, narrative, editing style, and audio. The tonality factors will be scored out of 10 so that the creator can choose to tweak their work based on any parameter. This project explores various attributes of videos to study the impact of multimedia promotions on the viewership indirectly by using revenue. The addition of video tonality to this prediction model should improve the prediction accuracy of the box office success of movies.