Projects

Developed a data-driven SQL model to enhance the SBA loan approval process. The project utilized historical data to analyze key factors such as loan amount, business location, loan term, and issuing bank, providing actionable insights for lenders. Key business questions included identifying factors that influence loan repayment, understanding how business location and loan terms impact approval rates, and assessing the likelihood of defaults based on different loan amounts. The primary goal was to predict loan repayment likelihood, enabling informed decision-making that supports responsible lending practices while increasing access to capital for qualified small businesses.

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Developed an AI-powered emergency response system to predict cascading disasters, detect fake social media alerts, and recommend real-time infrastructure action plans. Using historical sensor data, past disaster events, and zone-specific indicators, the system forecasts flood, fire, or earthquake severity while estimating energy disruption and casualty risk. Key features include a misinformation classifier for tweet validation, DBSCAN clustering for hotspot detection, and geospatial logic to match incidents to hospitals, fire stations, and shelters based on type and distance. Built a fully interactive Streamlit interface to visualize response zones, severity, and AI-generated action plans all live and explainable.

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Developed a data-driven machine learning model to predict customer churn in the telecom industry. The project utilized historical customer data, including account details, service usage, subscription plans, and customer service interactions, to identify key factors influencing churn. Actionable insights were derived to help telecom providers proactively retain customers. Key business questions included understanding the impact of service usage on churn, evaluating the role of customer service interactions, and assessing the influence of geographic location on customer retention. The primary goal was to predict the likelihood of churn, enabling data-driven decision-making that enhances customer retention strategies, reduces revenue loss, and improves customer satisfaction.

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I developed the Wallet Wise PowerBI Dashboard to monitor telecom revenue assurance and fraud management across multiple telecom services, including money, data, SMS, and voice accounts. The dashboard integrates real-time data from telecom systems to track key performance indicators (KPIs) and detect potential fraud, ensuring revenue integrity and minimizing financial losses. I designed the system to provide detailed insights on account usage, identify anomalies in revenue flows, and flag unusual patterns that could indicate fraudulent activity. It helps in monitoring customer behavior, visualizing service usage trends, and allowing for proactive measures to mitigate risks. By consolidating data from various telecom services, the dashboard enhances decision-making, enabling quicker responses to emerging issues and optimizing revenue collection processes.

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Conducted a comprehensive analysis of stock market behavior in response to the emergence of DeepSeek AI, leveraging Python, yFinance, and SEC EDGAR API to study AI-driven market fluctuations. The project involved retrieving historical stock data for key AI companies, performing sentiment analysis on SEC filings and disclosures, and correlating them with stock price movements and volatility trends. This analysis helped uncover the influence of AI-centric developments on investor sentiment and market response. By comparing patterns with past financial disruptions, the model identified potential indicators of AI-induced volatility. This project highlights the growing relevance of integrating NLP and financial data to monitor AI’s impact on capital markets, offering valuable insights for analysts and investors navigating tech-driven investment landscapes.

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