My profile says I’m a software engineer. Do I love to write clean, efficient code? Of course. Am I thrilled to learn new languages, technologies, and frameworks? Absolutely. But if you take a closer look, you’ll see there much more to me. Because I know that the quality of a product comes from the way we build it.
That’s why I’m constantly stepping into the shoes of our users – both by connecting with them to understand their needs and by putting my well-honed empathy skills into action. It’s how I help build products people love to use. And that’s why I understand that efficiency isn’t just the difference between O(n) and O(logn). It’s widespread collaboration, the recognition that the best products are molded by a diversity of insights. It’s constantly looking for opportunities to learn and get better at what I do. Because my ultimate goal is to always keep growing – and build some really cool stuff along the way. I’m always on the lookout for a new challenge, so if you need a fresh perspective, feel free to reach out.View Resume
The system not only aims to alert Dell if there is any unwanted user activity but also to maintain high availability. If any user tries to break through the dell.com payment process, submitting multiple numbers of credentials that include credit card number, CVV number, ZIP code, email ID, Name, Address within the same session, the system alerts Dell. Also, it keeps track of every user activity in dell.
A rising number of bike-share business causes more competition among each other. Several bike-sharing companies fail to sustain in this competitive market, due to lack of predictive analysis of their business. This project analyses data of the Los Angeles Metro Bike Share and successfully builds the predictive model to forecast future demand for bicycles,demand of each station for daily and hourly basis,classify the customers and clustering the station based on demand.
The Music symbols dataset contains 2128 clefs and 1970 accidentals. They were extracted from the modern and old music scores from the 19th century. The main goal of this experiment is to create a neural network that trains on the training data created from this dataset and then test the evaluation on the testing data. The goal is to predict whether a symbol is accidental or clef. For the second part, taking DoubleSharp as the class variable, the tests were conducted to classify them.
[HERE IS WHERE YOU REACH ME]Yes, Here!