Hi I'm Alvita Yathati.
Business Data Analyst
Welcome! I’m a graduate student at the University of Michigan, specializing in Information Systems and Technology. With expertise in Python, SQL, and Tableau, I’m passionate about turning data into actionable insights and collaborating with cross-functional teams to drive results. Take a look at my work, and feel free to reach out—I’d love to connect!
Projects
Personalized Music Recommendations for Enhanced User Engagement
MIT- Applied Data Science Program, Capstone Project, September 2024
Developed a music recommendation system using Collaborative Filtering, Content Based, Rank Based. Delivered tailored song suggestions based on user listening patterns, improving engagement and satisfaction on streaming platforms.
Facial Emotion Recognition for Enhanced Human-Computer Interaction
MIT- Applied Data Science Program, Deep Learning Project, August 2024
Developed a facial emotion recognition system using CNN, ResNet v2, and EfficientNet. Accurately classified facial expressions into key emotional categories, enhancing applications in healthcare, customer service, and human-computer interaction.
Automated Malaria Detection for Accurate Diagnosis
MIT- Applied Data Science Program, Deep Learning Project, August 2024
Developed a malaria detection system using a Convolutional Neural Network (CNN) and VGG16 transfer learning. Accurately classified parasitized and uninfected red blood cells from blood smear images, enhancing diagnostic speed and precision in healthcare applications.
Predicting Loan Defaults to Minimize Financial Risk
MIT- Applied Data Science Program, Practical Data Science Project, July 2024
Developed a loan default prediction model using multiple classifiers including Logistic Regression, Decision Tree, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Random Forest, AdaBoost, and XGBoost. Accurately identified high-risk applicants by analyzing key financial features, helping banks reduce default rates and make data-driven lending decisions.
Used Car Pricing with Machine Learning and Regression Techniques
MIT- Applied Data Science Program, Machine Learning & Regression Project, June- 2024
Developed a pricing prediction model using Machine Learning techniques, specifically linear regression with Statsmodels and Scikit-learn, identifying key factors . Utilized Pandas for data manipulation, Seaborn and Matplotlib for visualization, and performed multicollinearity checks with Variance Inflation Factor (VIF). Delivered actionable insights to optimize pricing strategies, helping Cars4U make data-backed decisions for increased profitability and customer satisfaction.
Optimizing Customer Segmentation for Targeted Marketing
MIT- Applied Data Science Program, Data Analysis & Visualization /
Unsupervised Learning Project May- 2024
Developed a customer segmentation model using K-means, DBSCAN, and Hierarchical Clustering to group customers based on spending patterns and demographics. Leveraged t-SNE for visualizing clusters and used PCA for dimensionality reduction. Delivered personalized marketing strategies tailored to each segment, enhancing engagement and improving campaign efficiency.
Predicting Passenger Satisfaction for the Shinkansen Bullet Train
Hackathon - August 2024
Developed a classifier model to predict passenger satisfaction on the Shinkansen Bullet Train using AdaBoost, Random Forest, and a Voting Classifier. Achieved 95.1% accuracy and secured the 16th rank by identifying key factors influencing satisfaction from travel and survey data, optimizing service strategies for improved passenger experience.
About
My name is Alvita Yathati, and here’s a little peek into my world!
Moving from India to the USA for my master's was a big shift, but I adjusted pretty quickly thanks to a great support system. I've also found community in the Society of Women Engineers, which has made settling in much easier. My journey into data science started in my undergraduate studies, where I got my first taste of machine learning through internships and projects. But it wasn't until later, during my master's course in information systems, that I rediscovered my love for data. I became fascinated by how data, when used right, can reveal so much. It's simple in theory but incredibly powerful when you dig deeper. Since then, I've been diving into more courses, reading articles from the MIT data science program I pursued, and keeping up with industry trends.
My life is a mix of creativity, curiosity, and staying connected with the people who matter most to me. I love losing myself in a good book, experimenting with new recipes, or sketching when I need a break. Books like The Great Gatsby, The Book Thief, and No Longer Human have really stuck with me—I often find myself thinking about their characters and themes, even in everyday moments. Cooking is another thing I enjoy, especially when my friend and I try our hand at dishes from Indian, Korean, and Mexican cuisines. It's fun to get creative in the kitchen. When I need to relax, I'll pick up my embroidery or go outside to sketch.
Looking ahead, I want to keep growing in data science—maybe even in a research role where I can explore new ideas. At the same time, I want to ensure that my personal life grows alongside my career. Whether learning new skills, trying out new recipes, or simply enjoying time with friends and family, I want to keep evolving and embrace the opportunities and experiences that come my way.
