Projects

1. Advanced Stock Market Prediction

Stock markets are highly volatile, influenced by economic indicators, investor sentiment, and external events. Traditional forecasting models struggle with accuracy due to market complexity. This project aimed to enhance predictive reliability by integrating machine learning, deep learning, and NLP to analyze structured and unstructured financial data for better investment decisions.

Multi-Algorithm Framework

Developed an ensemble learning model combining Regression, LSTM, CNN, and NLP to capture historical trends, sentiment shifts, and price fluctuations.
LSTM was trained on time-series stock data, while CNN extracted key technical indicators and chart patterns.
NLP processed real-time financial news and investor sentiment from social media, improving overall prediction precision.

Sentiment Analysis

Leveraged APIs from Twitter, Bloomberg, and financial news sources to analyze investor sentiment in real time.
Implemented VADER and BERT-based sentiment classifiers to assign weighted sentiment scores, correlating them with price movements.
Incorporated event-based anomaly detection to identify potential market shifts, enabling proactive decision-making.

Automated Data Pipeline

Designed a scalable ETL pipeline using Python, Pandas, and SQL to handle large volumes of historical and real-time stock data.
Integrated scikit-learn and TensorFlow to automate model retraining based on new data inputs, ensuring adaptability to changing market conditions.
Created interactive dashboards in Tableau to visualize insights, allowing traders to interpret model predictions and optimize investment strategies effectively.

2. Muli-Hedge Fund Performace Analysis

Hedge funds manage large portfolios with complex asset allocations, often struggling with volatility, risk assessment, and return optimization. This project aimed to enhance fund performance by identifying inefficiencies in portfolio diversification, forecasting potential risks, and providing data-driven strategies to optimize asset allocation.

Risk Assessment & Diversification

Analyzed a $500M hedge fund’s asset allocation to identify high-risk exposure during market downturns.
Used Principal Component Analysis (PCA) and Monte Carlo simulations to detect underperforming asset classes and propose rebalancing strategies.
Quantified risk exposure with VaR (Value at Risk) models and stress-tested portfolio performance under extreme market conditions.

ML for Predictive Risk Forecasting

Built Random Forest and LSTM-based predictive models to forecast monthly fund returns using macroeconomic indicators like interest rates, inflation, and GDP growth.
Achieved 90% accuracy in identifying high-risk periods, helping fund managers preemptively adjust positions to mitigate drawdowns.
Implemented Reinforcement Learning for dynamic asset allocation, optimizing portfolio performance based on evolving market conditions.

Data-Driven Decision Support

Designed interactive Tableau dashboards to visualize key fund metrics, including Sharpe ratio, alpha, beta, and drawdown trends.
Integrated SQL and Python (pandas, NumPy) for real-time data extraction and performance tracking.
Provided strategic insights that reduced annual drawdowns by $2M and improved the fund’s Sharpe ratio by 0.3, strengthening risk-adjusted returns.

3. AI-Driven Healthcare Live Analytics

Hospital readmissions increase healthcare costs and indicate gaps in patient care. This project aimed to leverage machine learning and data analytics to predict patient readmission risks and provide actionable insights to improve hospital efficiency and patient outcomes.

Patient Data Integration

Collected electronic health records (EHRs) from multiple hospital departments, including demographics, diagnoses, treatments, lab results, and discharge summaries.
Performed data preprocessing and feature engineering using Python (pandas, NumPy), extracting key indicators like length of stay, comorbidities, and medication adherence.
Utilized SQL for efficient data retrieval and storage, ensuring real-time access to critical patient information.

ML for Redmission

Developed predictive models using Random Forest, XGBoost, and LSTM to identify patients at high risk of readmission within 30 days.
Achieved 88% accuracy in forecasting readmission risks, enabling proactive intervention by healthcare providers.
Implemented SHAP (SHapley Additive Explanations) to enhance model interpretability, helping doctors understand key factors driving readmission.

Decision Support & Workflow Optimization

Created an interactive dashboard in Tableau to visualize patient risk scores, allowing hospital staff to prioritize interventions.
Integrated automated alert systems to notify doctors and nurses about high-risk patients at discharge, improving post-hospitalization care.
Reduced preventable readmissions by 17%, improving hospital resource allocation and patient recovery outcomes.

4. Optimizing Ad-Spend through Customer Insights

Marketing budgets often suffer from inefficient ad spend due to poor targeting and lack of actionable customer insights. This project aimed to analyze consumer behavior, segment audiences, and optimize ad allocation to maximize ROI and engagement rates across digital platforms.

Customer Segmentation

Aggregated consumer interaction data from Google Ads, Facebook Ads, website analytics, and CRM platforms to understand user behavior.
Employed clustering algorithms (K-Means, DBSCAN) to segment customers into high-value, mid-tier, and low-engagement groups based on purchase history, browsing patterns, and ad interactions.
Utilized SQL for efficient querying of structured ad performance data and customer demographics.

Ad Performance & Spend Optimization

Conducted A/B testing on ad creatives, messaging, and placement strategies to determine the most effective combinations.
Built predictive models using Random Forest and XGBoost to forecast conversion rates, click-through rates (CTR), and customer lifetime value (CLV).
Integrated Google Data Studio and Tableau dashboards to visualize ad effectiveness, helping marketers allocate budgets dynamically.

Automated Bidding & Budget Allocation

Implemented automated bid adjustments using Google Ads API and Python scripts, dynamically reallocating budgets toward high-performing audience segments.
Developed a real-time monitoring system using Power BI, alerting teams to underperforming campaigns and recommending spend reallocation.
Achieved a 25% reduction in customer acquisition cost (CAC) while increasing return on ad spend (ROAS) by 30%, ensuring higher engagement and improved profitability.