R Madhupreetha

A technically astute Engineering student, with a keen interest in Data Analytics and Machine Learning. Seeking opportunities to apply my problem solving expertise and knowledge in data science and machine learning to innovative and challenging projects, with a commitment to continuous professional development.

• E-Commerce Clothing Classification Model Development

Built a clothing categorization model with Keras and TensorFlow, using data augmentation (rotation, zoom, flipping) for  improved generalization. The modelʼs convolutional and pooling layers minimized overfitting, while a prediction function streamlined clothing classification, enhancing shopping experiences and inventory management.

• Sentiment Analysis on Social Media Data

Developed a sentiment analysis system for social media posts using Python and NLP libraries (NLTK, TextBlob). Implemented text preprocessing, tokenization, and sentiment classification to categorize posts as positive, negative, or neutral. Visualized sentiment distributions using Matplotlib and Seaborn to derive actionable insights. Enhanced understanding of natural language processing, data cleaning, and data visualization techniques.

• Customer Segmentation through Credit Card Transaction Analysis

Analysed credit card transaction data to identify customer segments using unsupervised machine learning (K-means,  DBSCAN). Applied evaluation metrics (Elbow method) and pre-processed data with Pandas and NumPy. Visualized insights with Matplotlib, Seaborn, and Plotly to support customer engagement, anomaly detection, and marketing strategies for  financial institutions.

• Demand Forecasting Model for Inventory Optimization in the Food Delivery Sector

Developed a demand forecasting model for the food delivery sector using Python and machine learning algorithms like Random Forest, XGBoost, and Gradient Boosting. By applying feature engineering on synthetic transaction data and  evaluating models with RMSE and MAE, the project improved predictive accuracy. This helps optimize inventory management,  reduce waste, and enhance customer satisfaction by accurately forecasting demand in a dynamic market.

• Hospital Repository

Designed a Hospital Repository using Python programming and MySQL.

• Summer Research Internship at CVRDE, DRDO
• Summer Live Project with Schneider Electric

• Data Science and AI Bootcamp - UpGrad
• Introduction to Programming the Internet of Things (IOT), University of California, Irvine
• Cyber Security, Completed a short course on Cyber Security by eLearning College.

• Manager, Sensors Workshops
• Manager, Festember Organizing Committee
• Manager, Publicity Team, Social Council
• Manager, Public Relations and Logistics, Pragyan
• Deputy Manager, SCIEnT
• Campus Ambassador for the London based newsletter “The Jurni”