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Bestine O. AI, Machine Learning and Data Platforms

My name is Bestine O. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: Machine Learning, Data Science, Python, SQL, Streamlit, etc.. I hold a degree in Bachelor of Technology (BTech), . Some of the notable projects I’ve worked on include: GenAI Projects Without the Clutter: Build Smart, Not Messy, Retail Basket Analysis. I am based in Mombasa, Kenya. I've successfully completed 2 projects while developing at Softaims.

I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.

I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.

I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.

Main technologies

  • AI, Machine Learning and Data Platforms

    5 years

  • Machine Learning

    1 Year

  • Data Science

    4 Years

  • Python

    4 Years

Additional skills

Direct hire

Potentially possible

Previous Company

Safaricom

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Experience Highlights

GenAI Projects Without the Clutter: Build Smart, Not Messy

This article guides developers on building clean, focused GenAI projects using RAG pipelines and LLM agents. I broke down complex AI concepts into simple, actionable advice for better planning and execution. The piece helped readers reduce technical clutter and sparked interest from collaborators looking to apply the same principles in real-world use cases.

Retail Basket Analysis

This market basket analysis project uses an online retail dataset to identify patterns in customer purchasing behavior. Implemented in Python with the Apriori algorithm and the `mlxtend` library, it focuses on finding frequent itemsets and generating association rules. The analysis suggests related products to customers, helping retailers make data-driven decisions on product recommendations, bundling, and promotions. This enhances customer experience by recommending complementary products and improving inventory management while increasing sales

Education

  • Technical University of Mombasa

    Bachelor of Technology (BTech) in Electronic engineering

    2016-01-01-2022-01-01

  • Kaggle

    in Computer Vision

Languages

  • Arabic
  • English
  • Swahili

Personal Accounts