About Me
Hi, I'm Afzal Mohamed, a Data Scientist with a unique background in visual effects (VFX). After over 7 years of crafting visually stunning content in the VFX industry, I transitioned into data science to further explore my passion for problem-solving and Python automation. My journey has shaped me into a detail-oriented and innovative data professional, eager to apply my diverse skill set to tackle complex business challenges.
With a solid foundation in machine learning, statistical analysis, and data visualization, I’ve honed my ability to derive actionable insights from large, complex datasets. From building regression models that predict house prices to analyzing sales trends and forecasting future demand, my projects have consistently demonstrated my capability to drive tangible business outcomes.
I’m well-versed in various machine learning algorithms, including Random Forests, SVM, kNN, XGBoost, and more. Additionally, I have a deep understanding of the data wrangling process, including feature engineering, data cleaning, and model evaluation. I believe that transforming raw data into business intelligence not only improves operational efficiency but also unlocks new opportunities for growth.
Throughout my career, I’ve developed an ability to deploy models and automate processes, ensuring that the solutions I create are not only accurate but also scalable and deployable. Whether it’s through interactive dashboards in Power BI or real-time predictive applications in Streamlit, I am passionate about creating tools that empower decision-makers to make informed choices.
Beyond technical expertise, I am committed to continuous learning and collaboration. I work closely with stakeholders to ensure that my solutions are aligned with business goals and are implemented effectively across teams. Whether you're looking to optimize operations, improve customer experiences, or make data-driven strategic decisions, I’m excited to partner with you to turn data into value.
Check out my projects to see how I solve real-world problems using data.