Jestin Edoor

About Me

I'm Jestin, a dedicated data analyst with over two years of hands-on experience in risk management and financial services. I excel at tackling complex data challenges, delivering actionable insights, minimizing risks, and creating substantial value for organizations and their clients. My deep passion lies in transforming raw data into innovative, strategic solutions that drive impactful and measurable results across various industries.

My hobbies include diving into a wide range of reading materials, from captivating fiction to insightful non-fiction books. I also enjoy immersing myself in video games, watching a variety of movies and TV shows, exploring nature on invigorating hikes, and relaxing with my favorite music playlists.

Projects

Insurance Risk Analysis Using Property Data in Panama City, Florida (Sep 2024 – Dec 2024)

Partnered with Precisely to analyze property and address data across 10,000+ properties for precise insurance risk assessment.
Leveraged flood zone classifications and geodemographic data to enhance accuracy of insurance risk profiles by 25%.
Implemented machine learning clustering to categorize properties by risk factors like floods and hurricanes, improving risk identification efficiency by 30%.
Empowered insurance companies to prioritize and mitigate high-risk properties, reducing potential claims exposure by an estimated $2M annually.
Developed a scalable model for future property evaluations, streamlining risk assessment processes by 20%.
Delivered actionable insights that supported insurers in minimizing financial losses from natural disasters, projecting a 15% reduction in loss ratios.
View the Presentation

AIG Share Price Analysis Using Time Series Forecasting (Apr 2024 – Jun 2024)

Collected and preprocessed historical AIG share price data spanning 5 years using R programming language, ensuring data quality for advanced modeling.
Developed and deployed ARIMA, SARIMA, ETS, and LSTM models, achieving a forecasting accuracy of 96% for AIG share price predictions.
Visualized forecasted trends and insights, enabling stakeholders to anticipate market movements and inform investment strategies with 90% confidence in trend predictions.
View the full AIG Stock Price Prediction Report (PDF)

Time Series Analysis of Meta Stock Returns (July 2025)

Conducted a time series analysis of Meta Platforms’ stock returns from 2021 to 2023 using Python libraries including pandas, matplotlib, and statsmodels to evaluate statistical properties and forecast future performance.
Investigated potential cyclical patterns around quarterly earnings—specifically, whether the stock tends to decline before earnings releases and rise afterward. Analysis supported this hypothesis: because Meta does not pay dividends, positive earnings surprises were often followed by post-call stock price increases.
Applied time series models such as ARIMA and GARCH to model and predict returns.
Identified ARIMA(2, 0, 2) as the best-fitting model based on diagnostic metrics and residual behavior.
Validated model performance using the autocorrelation function (ACF); residuals fell within the 95% confidence bounds, indicating strong model reliability and a good fit.
Presented results through clear graphs and visualizations to enhance interpretability and communicate key insights effectively.
View the Time Series Analysis Notebook

Customer Segmentation for E-Commerce (Jan 2025 – Mar 2025)

Analyzed customer purchase data to segment users based on behavior and demographics using clustering algorithms.
Improved targeted marketing strategies, increasing customer engagement by 20%.
Developed interactive dashboards to visualize customer segments for stakeholders.
View the Project Details

Predictive Maintenance for Manufacturing (Apr 2025 – Jun 2025)

Built machine learning models to predict equipment failures using sensor data.
Reduced downtime by 15% through proactive maintenance scheduling.
Presented findings to optimize operational efficiency for manufacturing plants.
View the Project Details