Aiswarya Raghavadesikan

Highly motivated and detail-oriented Data Analytics Master's student, actively seeking for internship/ co-op opportunities in the field of Data analytics/Machine learning/Data science.

• Driven by a passion for data analytics and a track record of leveraging Python and SQL to drive insights.
• Proficient in machine learning techniques and visualization, excel in transforming data into actionable strategies, demonstrating efficiency improvements in project outcomes.
• Learn and grow alongside a collaborative team while applying my data analytics skills to solve real-world problems.
• Strong problem-solving skills and attention to detail.
• Organized, dedicated team player and a dependable candidate, successful at managing multiple priorities with a positive attitude.
• Willingness to take on added responsibilities to meet team goals.

National Oceanic and Atmospheric Administration (NOAA) Weather Data Analysis; AWS based Data pipeline

• Performed end to end data pipelining using AWS framework, designed AWS glue and crawlers.
• Accomplished data orchestration using Apache Airflow for weather prediction and analysis using Python and created an interactive dashboard to visualize the inferences using AWS QuickSight.
• Created database using the AWS Redshift and executed queries.
• Achieved a highly accurate (>90%) weather prediction model for any given time during the year by using the real-time and historic NOAA weather data.

Subreddit comments classification

• Performed Sentimental Analysis using NLP and ML technologies to classify positive and negative comments by creating labels.
• Successfully Implemented various ML algorithms, achieved a testing data accuracy of 72% and precision of 78% for Random Forest and Support Vector Machines (SVM) classifiers that manifested appropriate comment classification.
• Created interactive dashboards using Streamlit to quickly determine the post classification outcome and various ML model's performance.

Enhanced Transformers (BERT) based Sentimental Analysis on Amazon Product Reviews

• Derived the labels using LLM techniques and applied the BERT to solve more relevance in sentimental classification.
• Implemented and evaluated various ML algorithms such as Logistic Regression, Random Forest and Gradient Boosting classifier, achieved 90% overall accuracy from Logistic Regression.
• Enabled quick decision making for reviewers thereby enhancing customer's shopping experience.