Features
.tif
and .pdf
legal documents.json
or .csv
with confidence scoresTech Stack
TECH_STACK: Python 3.11, TensorFlow / Keras , Pandas, NumPy,Matplotlib, Seaborn ,LSTM Neural Network, .h5
(Keras model serialization format), Wandb (Weights & Biases) for experiment tracking. Deployment: Streamlit , Hugging Face Spaces, Version Control, Git & GitHub, miniconda
An AI-powered text analytics pipeline designed to help financial analysts and compliance teams process high volumes of textual analyst commentary. identify if the commentary is Positive / Neutral / Negative, determine whether it contains risky financial signals, and extract key entities like company names or financial instruments — all through a single, unified API.
Tech Stack:
Language & ML: Python 3.11, scikit-learn, XGBoost , Data Processing: pandas, numpy, CountVectorizer
NER: Azure Cognitive Services (Language API) . Model Serving: FastAPI, Uvicorn
Deployment: Docker, Azure Container Registry, Azure App Service
Versioning & Testing: joblib, PyTest .Visualization (for EDA phase): matplotlib, seaborn (Jupyter)
GitHub:https://github.com/ecubeproject/Equity_Risk_And_Sentiment_Intelligence_Engine
VIEW PROJECTCampaign Success Prediction – End-to-End ML Deployment on GCP Vertex AI