Highly motivated Data analyst with a passion for unearthing hidden patterns and crafting visually engaging stories that empower informed decision-making.Eager to contribute data-driven solutions to meaningful challenges,…
Highly motivated Data analyst with a passion for unearthing hidden patterns and crafting visually engaging stories that empower informed decision-making.Eager to contribute data-driven solutions to meaningful challenges, seeking a data analyst position that fosters continuous learning and allows me to make a tangible impact on business outcomes.
Spearheaded pizza sales forecasting, analyzing market trends, consumer behavior, and external factors to improve demand prediction accuracy.
Utilized Power BI to create interactive dashboards, providing real-time insights into sales metrics, customer preferences, and competitor analysis.
Enabled sales team to make faster, data-driven decisions, resulting in a 5% increase in monthly revenue.
Leveraged ARIMA models and sentiment analysis techniques to identify key factors influencing pizza demand.
Leveraged Python and Pandas to efficiently clean and analyze diverse datasets, including one for Iris flower classification where I built an accurate machine learning model using decision trees.
This project honed my data wrangling and feature engineering skills.
Developed Power BI dashboards visualizing terrorist activities by region and attack count per country, enabling stakeholders to identify hotspot areas and implement targeted prevention strategies.
Analyzed terrorism datasets by country using unsupervised machine learning techniques to identify patterns and risk factors.
This informed recommendations for improved security measures, potentially reducing potential threats by 4%.
Developed sales prediction models using Python and Pandas to forecast profit and loss with 95% accuracy.
This enabled the foundation to optimize resource allocation and increase fundraising success by 6%.
Utilized Power BI to create storyboards visualizing financial trends, enabling the finance team to identify cost-saving opportunities and recommend a 10% budget reduction in non-essential areas.
Leveraged advanced techniques in both tools, including custom maps for regional sales analysis and interactive filters for dynamic data exploration.
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