This is a fun project where I applied Gatys' artistic neural style transfer algorithm with silhouettes and doodles to generate novel patterns and intricate art. The silhouettes facilitated the algorithm to give a neat finish to the style. Documentation and code are provided here.
This is a synthetic dataset for ridesharing that shows the details of driver sign-up data. The data consists of when the drivers signed up, the city, sign-up OS, sign-up channel (paid, referred, etc.). Once they signed up for the driving, they are requested to be available for background check, add their vehicle and once those stages are cleared, they are then allowed to start picking up passengers.
Here, the company wants to maximize the trips taken by the drivers. There may be several hurdles before first trip is taken, such as, the background check may be negative, or the drivers may fail to add their vehicle, and so on. This data analysis will try to find the important factors that is involved in the first trip taken by the drivers. And consequently, some recommendations are suggested based on the analysis.
Click here for the code and dataset.
I created this Shiny App in 2014 based on my research on consumer behavior and infrastructure analysis. The backend of this model is based on a multinomial logistic regression algorithm, which takes in consumer characteristics, vehicle attributes, and predicts the probability of the consumer purchasing a vehicle technology. The App gives the top 3 optimal choices for the consumer.
This was done as part of the Coursera Data Science Specialization project. Based on the corpus of text data given, n-grams were created. The tool predicts the next word in real-time based on the word chains given in the input. Model details are given under the 'Model' tab in the app.
Click here to play with the next word prediction tool.
This is a simple random forest classification done in R, to identify the conversion of a website.