Shyamashrita Chatterjee

Shyamashrita Chatterjee India |-| linkedin.com - LinkedIn: linkedin.com/in/shyamashrita Professional Summary Results-driven Data Analyst with expertise in machine learning, automation, and data-driven decision-making. Skilled in Python (Pandas, NumPy, Scikit-learn), SQL, Flask, and Power BI, with hands-on experience in predictive modeling, anomaly detection, and NLP-based sentiment analysis. Proficient in ETL (Extract, Transform, Load) processes, handling large datasets and optimizing SQL queries for efficiency. Experience in supervised & unsupervised machine learning, including classification, clustering, and regression models using Scikit-learn and XGBoost. Skilled in automation workflows with UiPath and Power Automate, integrating APIs for seamless business processes. Strong data visualization skills, creating interactive Power BI dashboards for stakeholders. Knowledgeable in time-series forecasting, financial anomaly detection, and deep learning-based text analytics (NLP). Passionate about AI-driven analytics to improve business intelligence and optimize operational efficiency. Skills Programming & Data Analysis ✔ Python (Pandas, NumPy, Scikit-learn) | R | Flask ✔ Machine Learning (K-Means, SVM, Decision Trees, NLP) ✔ Web Scraping (BeautifulSoup, Selenium) Automation & Databases ✔ UiPath | Power Automate ✔ SQL (PostgreSQL, NoSQL, DBMS) Data Visualization & Reporting ✔ Power BI | Matplotlib | Seaborn Work Experience PwC Senior Analyst (Apr 2023 - Present) • • Email Processing for Approval & Data Requests: o Processed unstructured email data, identifying patterns in approval workflows. o Built NLTK-based models to classify emails as approval, rejection, or request. o Developed Python scripts to extract key phrases and entities from emails for automation. o Automated 85% of repetitive email approvals, reducing human intervention. End User Automation for Access Management: o Designed UiPath & Power Automate workflows to automate tool access verification. o Integrated an internal e-learning platform API to confirm training completion. o Reduced manual verification efforts by 80%, minimizing onboarding delays. o Improved compliance and reduced unauthorized access risks. Analyst (Jul 2022 - Mar 2023) • Journal Entry Anomaly Detection: o Researched financial transactions, credit-debit patterns, and voucher types. o Built an anomaly detection system using One-Class SVM, Isolation Forest, and XGBOD. o Automated fraud detection, reducing investigation time by 40%. o Developed Power BI dashboards to translate ML outputs into auditor-friendly insights. Intern (Feb 2022 - Jul 2022) • Sentiment Analysis for Customer Reviews: o Preprocessed large customer review datasets with NLP techniques. o Used K-Means Clustering to classify feedback into Positive, Negative, and Neutral. o Designed Power BI dashboards to visualize sentiment trends. o Improved classification accuracy by 15%, enhancing customer satisfaction analysis. Projects 1. Fake News Classification using Machine Learning ✔ Built a Fake News Classifier using SVM, Decision Trees, and Bag-of-Words techniques. ✔ Applied TF-IDF vectorization, Word Embeddings, and Lemmatization for text preprocessing. ✔ Achieved 92% classification accuracy with SVM, improving misinformation detection. ✔ Deployed a Flask-based API to enable real-time news classification. 2. Stock Price Analysis & Prediction using Web Scraping ✔ Scraped Yahoo Finance using BeautifulSoup & Selenium, collecting real-time stock prices. ✔ Designed a PostgreSQL database to store and process stock price fluctuations. ✔ Applied ARIMA & LSTM models for time-series forecasting and trend prediction. ✔ Created Power BI dashboards to visualize volatility trends, moving averages, and stock predictions. ✔ Improved forecasting accuracy by 18% using LSTM neural networks. 3. Sentiment Analysis Web Application ✔ Built an AI-powered web app for analyzing sentiment in text-based user input. ✔ Used VADER NLP for real-time sentiment classification. ✔ Developed a Flask-based application with a user-friendly HTML, CSS, and Bootstrap front-end. ✔ Integrated NLTK (Natural Language Toolkit) for text preprocessing (stopword removal, stemming, and lemmatization). ✔ Deployed on AWS Lambda for low-latency, real-time analysis. Education • B.Tech in Computer Science Engineering University of Engineering and Management, Jaipur (2018 - 2022) • AISSCE (XII) – CBSE Delhi Public School, Ruby Park, Kolkata (2018) • AISSE (X) – CBSE Amrita Vidyalayam, Durgapur (2016) Certifications & Achievements Hacktoberfest 2020 – Submitted 4+ PRs 3rd Place in CODE JARVIS (Technical Fest, UEM Jaipur) International Mathematics Olympiad – Secured 239th rank internationally 2nd Place in CIRCUIT HUNT (Technical Fest, UEM Jaipur)