Projects

This project aimed to determine the effectiveness of a marketing ad campaign in increasing conversion rates. Using a dataset from Kaggle, we conducted an A/B test to compare the conversion rates of users exposed to the ad (treatment group) with those who were not (control group). Through data wrangling, hypothesis testing, and statistical analysis (bootstrapping, binomial sampling, and two proportion z-test), we found that the ad campaign significantly improved conversions. Additionally, a logistic regression analysis confirmed the statistical significance and practical impact of the ad campaign.

This project delves into the intricacies of aircraft selection and flight frequency distribution to minimize daily operational costs for a budget airline. Leveraging the Analytic Hierarchy Process (AHP) for aircraft selection and Linear Programming (LP) for route optimization, we achieved significant cost reductions across 19 domestic destinations.

The project sheds light on the significance of IPO and motivations. We explore the IPO process, from company motivations for going public to the intricacies of analyzing IPOs using both fundamental and technical criteria.

This research paper explores the changing retail landscape, with a focus on "Warehouse clubs and Supercenters" and "E-commerce and online auctions" in NAICS. It assesses the impacts of supply chain disruptions and evolving consumer behavior. Using PESTEL and the five-force framework, it analyzes Walmart's business model, suggests improvements for sustained relevance.

Developed a machine learning model using Logistic Regression, Decision Trees, and Random Forest to predict loan eligibility based on customer data. Conducted in-depth exploratory data analysis, data cleaning, and preprocessing, resulting in a highly accurate model with credit history and co-applicant income as significant factors

This project focuses on developing a sales forecasting model for Walmart, leveraging historical sales data to enhance inventory management and supply chain optimization. By identifying key correlations and providing actionable recommendations, the project aims to improve Walmart's operational efficiency and boost sales.

The project aimed to optimize marketing strategies, enhance customer satisfaction, and improve resource allocation by developing Logistic Regression. Key features included Income, Age, Family, CCAvg, CD Account, Mortgage, Securities Account, Credit Card, and Online Banking. The model achieved high performance with an accuracy of 0.934 and an ROC AUC of 0.936. Income emerged as the most significant predictor of loan acceptance. This data-driven approach provides valuable insights for strategic decision-making in the banking industry.

In this project, I developed two Power BI dashboards—Overview and Details—to analyze car sales data. The dashboards provide real-time insights into key metrics such as Year-to-Date (YTD), Month-to-Date (MTD), and Year-over-Year (YOY) growth. Using interactive visualizations like line charts, pie charts, and map charts, the project helps track sales trends, distribution by car body style, color, and region, and offers a detailed data grid for all sales.

This project aimed to enhance property valuation accuracy for condominiums in Dallas using a data-driven approach. We developed a predictive model using multiple linear regression, leveraging key features such as Bedrooms, Bathrooms, Square Footage, Lot Size, and Property Type. Despite not achieving the target R² value of 0.9, the analysis revealed that Square Footage is the strongest predictor of property prices. The project highlights the potential of machine learning in improving real estate valuations and provides insights for future model enhancements.

This project focuses on developing an LSTM-based machine learning model for predicting the future stock prices. By analyzing historical stock data, building a robust predictive model, rigorously evaluating its performance, and testing its ability to provide continuous predictions, the goal is to create a powerful tool for investors and financial professionals.