Sowmya Lanka
Data Scientist | ML, NLP & GenAI Solutions
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What I built
Developed a machine learning solution to predict Buy Box and sales prices using historical analytical data and Linear Regression.
How it works
Analyzed the data for linear relationships, autocorrelation, multicollinearity, homoscedasticity, and normally distributed errors.
Applied Principal Component Analysis (PCA) for dimensionality reduction and to improve model performance.
Identified key business factors influencing price prediction.
Evaluated the model using R², Adjusted R², RMSE, MAE, and MSE metrics.
Technologies
Python · Pandas · NumPy · Scikit-learn · Linear Regression · PCA
Outcome
Built a price prediction model that identified important business drivers and evaluated prediction performance using multiple regression metrics.
0
51
What I built
Developed a Natural Language Processing (NLP) solution to detect DI Flag issues by analyzing and classifying text descriptions.
How it works
Cleaned text data by removing hashtags, HTML tags, special characters, and numeric values.
Applied text preprocessing techniques including tokenization, stop-word removal, stemming, and lemmatization.
Converted text into numerical features using Bag of Words (BoW) and TF-IDF.
Trained and evaluated the model using accuracy and confusion matrix metrics.
Performed 30 days of validation testing before production deployment.
Technologies
Python · NLP · Scikit-learn · TF-IDF · Bag of Words · Pandas · NumPy
Outcome
Built a text classification solution that helped identify DI Flag issues from descriptions and validated the model's performance before deployment.
0
45
What I built
Developed an automated clinical text summarization solution using Google Gemini AI and prompt engineering to generate concise summaries from lengthy clinical chart notes, transcripts, and unstructured medical text.
How it works
Processed lengthy clinical notes and transcripts.
Used prompt engineering to guide Gemini in identifying important medical information.
Extracted key details such as symptoms, diagnoses, medications, and treatment plans.
Generated concise, readable summaries from the original clinical text.
Technologies
Google Gemini AI · Python · Prompt Engineering · NLP · Generative AI
Outcome
Reduced clinical chart review time by approximately 30%, helping healthcare professionals identify important information more quickly and efficiently.::
0
45
AI-Powered Policy Q&A Assistant
Built a Retrieval-Augmented Generation (RAG) chatbot using Azure OpenAI GPT to answer policy-related questions with accurate, context-aware responses.
What I built
Extracted and structured information from policy documents into a knowledge base.
Used Azure Cosmos DB for scalable information storage and retrieval.
Implemented RAG to retrieve relevant policy information based on user queries.
Integrated Azure OpenAI GPT to generate natural-language, context-aware answers.
Technologies
Python · Azure OpenAI · GPT · RAG · Azure Cosmos DB · LangChain
Outcome
The solution enables users to quickly find relevant information from policy documents without manually searching through lengthy documents.
0
60