Hello! I’m Abraraw Ayal, a dedicated data science student at Bahir Dar University. My journey in data science began with a curiosity for uncovering patterns in complex datasets, leading me to master machine learning, statistical analysis, and data visualization. I’ve worked on projects tackling real-world challenges like unemployment forecasting and sales prediction, using tools like Python and SQL. My goal is to leverage data to drive impactful solutions in industries like healthcare and finance.
I’m proficient in end-to-end data pipelines—data collection, cleaning, modeling, and deployment—and I thrive on turning raw data into actionable insights. Currently, I’m exploring advanced topics like deep learning and big data frameworks to push my skills further.
Developed an SVM model using Scikit-learn to classify iris species above 95% accuracy, deployed via Gradio. Dataset: UCI Iris Dataset.
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Applied ARIMA to forecast unemployment trends during COVID-19 using World Bank data, achieving a 12% error margin.
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Built a linear regression model to predict future sales with best accuracy using historical retail data.
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Used feature engineering and random forest regression to predict used car prices with a 10% error rate. Dataset: Kaggle Car Dataset.
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Implemented K-means clustering to segment customers for a marketing campaign, using a 500-row dataset from a retail client.
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