Machine Learning Data Analytics

Sneha Agrawal

• Cell: 857-***-**** • E-mail: *******@**.***, ***********@*****.*** • LinkedIn • GitHub • Advance Excel TECHNICAL SKILLS

• Programming: Python (Pandas, NumPy, Matplotlib, Seaborn, Streamlit, Scikit-Learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch, NLTK, BERT, PySpark, BeautifulSoup, SMOTE) R (dplyr, tidyr, ggplot2, plotly, caret). Experience with Weka, JMP, Docker, Git.

• Databases, Visualization: SQL (PostgreSQL, SQL Server, Oracle), Tableau, Power BI, Microsoft Excel, Plotly.

• Statistical Analysis: Hypothesis Testing, Probability Distributions, Bayesian Statistics, Regression Analysis (Linear, Logistic), Classification Analysis, A/B Testing, NLP, Regularization, Normalization, Optimization, Statistical Modeling, Financial Modeling, Business Operations Analytics.

• Cloud Technology: AWS (S3, EC2, EMR), Google Cloud (Google storage, Dataproc, Google Engine), Apache Spark and Kafka, Hadoop HDFS. EDUCATION

Boston University Boston, MA, USA

MSc in Applied Data Analytics September 2023 – Present

• Relevant Coursework: Big Data Analytics, Advanced Machine Learning and Neural Networks, Data Mining, Data Science with Python, Data Structures & Algorithms, Web Mining & Graph Analytics, Database Design & Implementation, Analytics and Data Visualization.

• Role: Teaching Assistant, delivering in-depth explanations of machine learning algorithms and statistical methods during office hours, contributing to a 25% increase in student comprehension and confidence in using R for assignments. St. Xavier's College Mumbai, Maharashtra, India

Ba in Economics and Commerce June 2017 – October 2020

• Engaged in a comprehensive study of economics, with electives in statistics, mathematics, and commerce. EXPERIENCE

ICICI Bank Ltd. New Delhi, Delhi, India

Business Loan Relationship Manager November 2020 - May2022

• Achieved a 20% rise in cross-selling of financial products and boosted client satisfaction by 15% of high-net-worth wealth management clients.

• Drove a 25% increase in loan origination within the first year by conducting comprehensive financial analysis for working capital solutions.

• Enhanced financial accessibility for low-income groups by leading a micro-insurance project, utilizing Tableau to visualize client data and identify loan acquisition barriers, resulting in a 40% increase in client engagement and inclusion.

• Developed key skills in financial analysis, client relationship management, and strategic planning that enhanced performance and teamwork. ACADEMIC PROJECTS

Apache Kafka October 2024

• Accelerated deployment speed by 35% by building real-time data streaming demos, enhancing distributed data handling.

• Processed and consumed data in real-time using Python scripts, showcasing Kafka's core capabilities and enabling faster decision-making.

• Streamlined event tracking and user account management by integrating Kafka, PostgreSQL, and Streamlit, enhancing real-time system monitoring. Sentiment Analysis of Amazon Reviews May 2024

• Engineered an Streamlit app for real-time sentiment predictions on 10,000+ Amazon reviews, helping users make informed purchasing decisions.

• Attained 92% accuracy in sentiment classification using BERT and RoBERTa models, aiding businesses in understanding customer feedback.

• Boosted F1 scores by 15% through cross-validation and feature engineering, enhancing model reliability for user insights. International Students trend in USA April 2024

• Analyzed 7 datasets to identify key factors influencing international student enrollment, guiding strategic recruitment and policy-making.

• Mastered 85%+ predictive accuracy using Lasso Regression to tackle collinearity and Random Forest to support trend forecasting. Stroke Prediction March 2024

• Accomplished 90% accuracy in stroke prediction using weak learners and Random Forest, optimizing features with Recursive Feature Elimination.

• Applied SMOTE to balance data classes; this initiative resulted in a 40% increase in early detection and proactive patient care. Rider December 2023

• Engaged 500+ participants by creating a mobile application for aggregating ride-hailing rates, simplifying comparisons across platforms.

• Reduced time spent switching between applications by 30% with a unified interface, enhancing user experience and time management.

• Advanced data retrieval speed by 25% with SQL Server in Docker for database management, ensuring reliable ride history storage. Daily Salary Calculator November 2023

• Reduced manual processing time by 50% by automating daily salary calculations using Python, ensuring accurate payroll accuracy.

• Optimised reporting accuracy by 20% through unit testing and real-time attendance tracking, accounting for overtime and leave deductions.

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