We can organize an interview with Aldin or any of our 25,000 available candidates within 48 hours. How would you like to proceed?
Schedule Interview NowMy name is Usman K. and I have over 6 years of experience in the tech industry. I specialize in the following technologies: Machine Learning, Natural Language Processing, Data Science, NLTK, Python, etc.. I hold a degree in . Some of the notable projects I’ve worked on include: Twitter Sentiment Analysis. I am based in Rawalpindi, Pakistan. I've successfully completed 1 projects while developing at Softaims.
I approach every technical challenge with a mindset geared toward engineering excellence and robust solution architecture. I thrive on translating complex business requirements into elegant, efficient, and maintainable outputs. My expertise lies in diagnosing and optimizing system performance, ensuring that the deliverables are fast, reliable, and future-proof.
The core of my work involves adopting best practices and a disciplined methodology, focusing on meticulous planning and thorough verification. I believe that sustainable solution development requires discipline and a deep commitment to quality from inception to deployment. At Softaims, I leverage these skills daily to build resilient systems that stand the test of time.
I am dedicated to making a tangible difference in client success. I prioritize clear communication and transparency throughout the development lifecycle to ensure every deliverable exceeds expectations.
Main technologies
6 years
2 Years
5 Years
3 Years
Potentially possible
Systems Limited
This project was a personal project of mine which I did before joining SoftAims. It takes keywords and the number of tweets from the user. This Project Used the Twitter API v1 and fetches the tweets which contain user defined keywords. Those tweets are preprocessed, then transformed into their vector representation. They are then fed into the TF-IDF Classifier which classifies the vectorized form of the tweets into either positive or negative. this is done for all the number of tweets. and then the final graph is displayed displaying the percentage of a positive and negative sentiment of the topic.
in Computer science
2019-01-01-2023-01-01