Taruni Keshapally

Computer Science Master's graduate with expertise in data analytics. Demonstrated proficiency in SQL, Excel, Power BI, and Tableau for effective data visualization and reporting. Strong analytical and problem-solving abilities, driven by a passion to contribute to data-driven decision-making processes.

Supply Chain Risk Analysis: 07/24

• Designed a framework integrating IoT data and historical metrics, reducing risk identification time by 30%.
• Processed over 10,000 data points using APIs and web scraping, achieving 95% accuracy.
• Implemented predictive models, enhancing risk prediction accuracy by 25%.
• Built an interactive Tableau dashboard.

Climate Impact on Agriculture: 03/24

• Merged datasets from meteorological and agricultural sources to improve reliability and completeness.
• Identified trends, including a 15% increase in yield variability correlating with temperature changes.
• Utilized support vector regression to forecast crop yields, achieving a 20% improvement in accuracy.
• Developed a dashboard for policymakers, contributing to three adaptive agricultural strategies.

Text Analysis on Customer Support Tickets: 12/23

• Analyzed over 15,000 support tickets, uncovering major recurring issues and reducing resolution times by 20%.
• Employed NLP techniques to improve sentiment classification accuracy by 30%.
• Built machine learning models that cut manual ticket categorization time by 50%.
• Designed visualizations that led to two new customer support strategies, enhancing overall satisfaction.

Online Advertising Campaign Analysis: 10/23

• Automated data collection from Google Ads and Facebook Ads, improving efficiency by 40%.
• Analyzed campaigns, resulting in a 15% increase in ROI through data-driven adjustments.
• Developed a dashboard that increased actionable insights derived from campaign data by 25%.

Cryptocurrency Market Sentiment Analysis: 08/23

• Analyzed over 100,000 tweets about cryptocurrencies, enhancing the sentiment analysis dataset.
• Identified correlations between sentiment and price movements, achieving 80% accuracy in predictions.
• Built LSTM models that improved price movement forecasts by 15%.
• Created dynamic visualizations to aid traders in making informed decisions, enhancing trading strategy performance by 10%.