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Schedule Interview NowMy name is Nafeesah E. and I have over 2 years years of experience in the tech industry. I specialize in the following technologies: Data Analytics, Python, Machine Learning, FastAPI, Technical Writing, etc.. I hold a degree in Bachelor of Science (BS), Other. Some of the notable projects I’ve worked on include: Bitcoin Price Analysis and Real-Time Data API using FastAPI, Customer Churn Prediction Using Machine Learning, Customer Sentiment Analysis for Product Improvement. I am based in Kano, Nigeria. I've successfully completed 3 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
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This project aims to develop a comprehensive API for Bitcoin price analysis, providing both historical and real-time data. Built with FastAPI, the API integrates with multiple cryptocurrency exchanges like Coingecko, CoinCap, Binance, and Kraken to deliver accurate and up-to-date pricing information. By focusing on efficiency and scalability, this API serves as a valuable resource for developers, traders, and analysts, facilitating informed decision-making in the volatile cryptocurrency market.
The goal of this project was to predict customer churn for telecommunications companies using machine learning techniques. I implemented Random Forest and XGBoost models to analyse customer data and identify key factors influencing churn. By evaluating model performance with confusion matrices and classification reports, I provided actionable insights to help companies like MTN, Airtel, 9mobile and Outsource Global enhance their customer retention strategies. The project significantly improved decision-making processes, leading to potential revenue growth through reduced churn rates.
The goal of this project was to analyse customer reviews to determine their sentiment positive, negative, or neutral based on the text content of the reviews and associated metadata. Using Natural Language Processing (NLP) techniques, I extracted valuable insights into customer feedback, which helped identify product strengths and weaknesses. The analysis provided actionable insights that enabled the business to improve customer experience and refine product offerings, ultimately driving higher customer satisfaction.
Bachelor of Science (BS) in Statistics
2012-01-01-2016-01-01
Other in Post Graduate Diploma in Management
2020-01-01-2021-01-01