Technical Skillset

This project aims to analyze the factors influencing happiness scores worldwide using the World Happiness Report dataset. The analysis will begin with detailed exploratory data analysis (EDA) to uncover patterns in happiness indicators such as social support, life expectancy, freedom, generosity, and perceptions of corruption across various countries and regions. The project will utilize R for data cleaning and preparation (20%) and Python for in-depth analytics, including predictive modeling and statistical analysis (80%). By building predictive models, the project will seek to identify the key indicators that most significantly impact societal happiness, providing insights into ways to increase these scores. Anticipated challenges include managing a large dataset, handling missing values, and selecting the most effective model. This project presents a valuable learning opportunity in data wrangling, feature engineering, predictive and unsupervised modeling, and effective data visualization, ultimately strengthening technical skills in both R and Python

Tools Used

R Programming · Python Programming

This projectaims to develop a predictive model for Netflix (NFLX) stock prices and trading volume over a three-month horizon, providing valuable insights for investment decision-making and market trend analysis. Using historical daily stock data, including opening, closing, high, low prices, and trading volume from February 5, 2018, to February 4, 2022, the model will forecast daily closing prices and trading volume, helping to predict future market interest and stock liquidity. Expected outcomes include accurate stock price forecasts to support short-term investment strategies and volume predictions to gauge anticipated market activity. This project leverages statistical and machine learning approaches to model Netflix’s stock behavior, offering insights that can inform traders and analysts in navigating short-term market trends

Tools Used

SAS (Programming Language)

This project simulates the development of a Real-Time Disaster Management System to demonstrate project management skills in coordinating a complex data-driven initiative. The system is designed to monitor and analyze simulated disaster events—like earthquakes, floods, and storms—using data from virtual weather APIs and IoT sensors, allowing predictive modeling and risk analysis to assess potential disaster impacts. Dynamic dashboards and alert systems provide real-time decision-making support for agencies and responders, enhancing preparedness and response strategies. This simulation requires effective project management, including team coordination, resource allocation, deadline management, and communication protocols, showcasing the team's capability to integrate technical skills, maintain project alignment, and manage deliverables efficiently

Tools Used

Microsoft Project

This project leveraged the Real Estate Across the United States (REXUS) inventory, a comprehensive tool from the Public Building Services (PBS), to enhance the management of U.S. government real estate assets. By integrating data from REXUS and the Space Tracking and Reporting System (STAR), the project aimed to refine property management processes and increase operational efficiency. Using Microsoft Excel, a VBA-powered application was developed to allow users to filter properties based on ownership, parking availability, and other attributes. This tool generates tailored reports, enables data export, and integrates with Tableau for advanced visualizations, such as identifying optimal property locations by filtering on construction date, historical status, and property type. The application provides significant value for federal agencies and real estate professionals, enabling them to track underutilized spaces, optimize real estate portfolio management, and make data-driven decisions more efficiently. This project showcases skills in data cleaning, VBA programming, and advanced visualization, providing targeted insights and improving the efficiency of property management practices.

Tools Used

VBA Programming · Microsoft Excel · Tableau

Conducted a comprehensive data analysis for an online retail superstore to enhance decision-making and improve operational efficiency. This project involved designing a data warehouse, performing ETL (Extraction, Transformation, and Loading) processes, and creating a Fact Orders Cube to centralize data on orders, products, and customer information. Using SQL and Power BI, generated actionable reports to identify key business insights, including profitability by region, product category performance, and high-margin customer segments. Key findings included the highest profitability in Office Supplies and specific high-profit subcategories like Copiers, while categories like Machines and Tables showed loss trends. Leveraged SQL Server Reporting Services and Power BI to present annual, category, and segment-based insights for management, helping optimize inventory, pricing strategies, and targeted marketing efforts. This project showcases skills in data warehousing, ETL, SQL, and data visualization

Tools Used

Extract, Transform, Load (ETL) · Microsoft SQL Server · SQL Server Reporting Services (SSRS) · Microsoft Power BI · OLAP Cubes

This project developed predictive models to estimate resale prices for used Ford cars, providing Ford Motors with valuable insights to refine pricing and inventory strategies. A dataset of 17,966 records was prepared and cleaned, with outliers managed, categorical variables encoded, and the data reduced to 1,000 records for model feasibility. Three supervised learning models HPREG (High-Performance Regression), Decision Tree, and Neural Network were tested using an 80/20 training-validation split. The HPREG model achieved the best accuracy, identifying mileage, engine size, and transmission type as key predictors. The Decision Tree model highlighted age and engine size as significant factors but showed some overfitting. SAS Viya was used to visualize critical trends, such as the influence of car age and mileage on price, generating actionable insights. This project demonstrates expertise in predictive modeling, data preparation, and data-driven insight generation.

Tools Used

SAS Visual Analytics · SAS (Programming Language)

This project presents an innovative analytical approach for monitoring mental health trends and associated disorders across 20 countries from 2000 to 2018. Through the analysis of six comprehensive datasets, key patterns and trends are identified to inform evidence-based treatment strategies. Regression modeling and future projections reveal the significant impact of disorders such as alcohol use, bipolar disorder, and depressive disorder on mental health, emphasizing the need for targeted interventions. The findings underscore the importance of investing in mental health services and raising awareness to reduce global Disability-Adjusted Life Years (DALYs) rates. Future scope includes policy implementation to support individuals facing mental and substance use disorders, ultimately fostering healthier lifestyles and improved global well-being.

Tools Used

SAS Visual Analytics · SAS (Programming Language)

The "Cool Wheels Shipping Company" case study entails creating a database system with tasks like designing normalized tables, ERD diagrams, setting up a database with relationships and constraints, and inputting test data. It involves building user-friendly forms and reports, OLAP charts for data analysis, offering a practical application of database management skills in business.

Tools Used

Microsoft Access· Microsoft SQL Server ·

Built Arima, Sarima traditional model and RNN, LSTM, GRU, XGboost machine learning model for details analysis to see trends and seasonality of US Energy Production and Consumption. The dataset main dependent variables were consumption and production, and independent variables were import, export, industrial consumption, stock change, and time. GRU machine learning model outperformed as compared to all models.

Tools Used

Python Programming · Tableau Data Prep

To design and implement an automated mask detection system that uses machine learning techniques to identify individuals not wearing masks in public places, such as malls, railway stations, bus stops, and schools. The system aims to support public health initiatives by ensuring compliance with safety regulations regarding mask-wearing to reduce the spread of COVID-19. It is intended to operate in real-time, providing on-the-spot notifications for individuals without masks and managing the occupancy levels in various establishments according to government-mandated guidelines. Additionally, the system will utilize an ultrasonic sensor for counting the number of people entering a mall to ensure that the occupancy does not exceed the set limits for safe distancing. The project seeks to enhance community health safety by using technology to enforce preventative measures during the pandemic efficiently.

Tools Used

Python Programming· IOT Devices · Raspberry Pi 4

To develop a secure, efficient, and decentralized banking application utilizing blockchain technology that addresses the contemporary challenges of cyber-security and the inefficiencies in traditional banking systems. This project aims to leverage the inherent security features of blockchain, such as tamper-proof ledgers and decentralized consensus mechanisms, to create a banking system that is resilient to fraud and unauthorized access. The project also intends to implement smart contracts to automate transactions, thereby reducing the need for intermediaries and lowering transaction costs. Through this innovation, the project seeks to provide a robust platform for financial transactions that enhances trust, transparency, and accessibility for all users, while also exploring the integration of blockchain in areas beyond finance, such as IoT and smart systems, for a holistic improvement of digital interactions and processes.

To develop a sophisticated text analysis system that utilizes algorithms for subjective summarization to process and condense customer reviews on an e-commerce platform. The goal is to extract meaningful insights and sentiments from user-generated content, providing concise, actionable summaries that reflect the collective opinions and experiences of customers. This system aims to assist potential buyers in making informed decisions by presenting the essence of customer feedback without the need for them to go through extensive individual comments. For the e-commerce provider, the summarization tool is intended to identify trends in customer satisfaction and areas for product or service improvement. The project endeavors to enhance the shopping experience, improve customer satisfaction, and ultimately drive informed purchasing behavior through an intelligent summarization of vast amounts of review

Tools Used

HTML · CSS · Java · Java Script· XML