Computer Science graduate with hands-on experience in SQL, Python, BI tools, and data analysis. Worked on preparing raw datasets, building dashboards, and generating insights using tools like Power BI, Excel, and Python …
Computer Science graduate with hands-on experience in SQL, Python, BI tools, and data analysis. Worked on preparing raw datasets, building dashboards, and generating insights using tools like Power BI, Excel, and Python libraries. Strong understanding of data cleaning, reporting, and performance analytics with the ability to work across multi-source data. Detail-oriented, quick to learn, and focused on delivering accurate, business-impacting insights in fast-paced environments.
Completed a 6-week internship focusing on machine learning, data preprocessing and model building using Python and Scikit-learn.
Built and tested supervised models (Linear Regression, Decision Trees) on real-world datasets.
Evaluated 2+ machine learning models using Scikit-learn and visualized performance metrics with Matplotlib, achieving up to 92% accuracy in classification tasks.
Analysed structured datasets using Python, Excel and Power BI to generate insights and dashboards for business use.
Designed and implemented 5+ interactive charts to monitor 7 key performance indicators (KPIs), used by the marketing team for campaign analysis.
Built 3 dynamic Power BI dashboards to track monthly sales, reducing manual reporting time by 30% and improving decision speed.
Increased customer segmentation accuracy by 20% using association rule mining.
Applied Apriori algorithm on 100,000+ retail transactions to uncover 10+ high-confidence item combinations,
increasing cross-sell potential by 25%
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