• Developed machine learning models for a Legislative AI Interface, enabling users to interact with bills, perform QA, Summarization, and bill searches.• Designed and implemented RESTful APIs using FastAPI, managing various functionalities such as user authentication and subscription management.• Utilized PostgreSQL and SQLAlchemy for database management, including schema setup, models, and CRUD operations.• Integrated Google and Microsoft OAuth2 authentication, enhancing user convenience and security.• Implemented a robust subscription management system using Stripe.• Key Accomplishments:- Successfully integrated multiple third-party authentication methods, enhancing user convenience and security.- Developed and maintained comprehensive documentation, contributing to better team collaboration and project scalability.

• Taught Masterclass sessions on Data Analytics tools such as Power BI, Python, and SQL.• Designed and developed curriculums for Data Analytics courses.• Provided personalized guidance and support to students.• Managed training programs, ensuring seamless delivery and optimizing the learning experience.• Developed assessment methodologies to evaluate student progress.

After conducting a survey, I discovered that the majority of newcomers in Data Analytics field struggle to create outstanding portfolio projects. They also don't know how to integrate what they've learned from different platforms like Datcamp and Dataquest into a project. Some people do not know how to set up a coding workspace on their local PC.As a result, I now teach a seasonal class where I show students how to create portfolio projects. The course is entirely project-based.I discuss the following tools and programming languages: 1. SQL 2. Python 3. Power BI

• Developed and taught curriculum on data analytics using Python, SQL, and Power BI.• Assessed student progress and provided individualized feedback.• Created engaging learning materials, including presentations, exercises, and case studies.

Project - Currency Exchange Rate Prediction* Led a team of 13 people from four countries on a Capstone project.* We decided as a group to forecast the closing price of currency exchange, which resulted in the separation of each currency pair for forecasting.* Because this is time series data, we used the Dickey-Fuller test statistics method to check for stationarity.* We used Auto ARIMA to find the best values for parameters like p, d, and q. * We forecasted using both the ARIMA and Prophet models because the Prophet model performs better on time series with high seasonality.* We used Streamlit to deploy our model with an interactive user interface.

Project – Game of Throne Analysis• Led a team of 13 individuals from 3 different countries in a premiere project• Communicate and assign tasks to group members based on the project flow process• Host group meetings to discuss the progress and difficulties encountered while working on individual task• As a group we performed feature engineering which helped in training our classification models for prediction• Evaluated 5 classification models to choose the best based on the confusion matrix and ROC Curve diagram• Worked collaboratively with other members to tune the model three (3) times for the best performance using RandomSearchCV and GridSearchCV respectively• Designed an evaluating function to help evaluate the performance of the trained model• Presented our project during the premiere project presentation and answered questions from the judges.* Successfully reduced the base model error by 5.43% and increased accuracy by 2.42% after tuning the model * Higher accuracy in predicting characters' death for those that are in the house of allegiance

* Completed the course, Python for Machine Learning* Predicted energy used by appliances using regression models.* Predicted Electrical Grid Stability using classification models

• Taught students the fundamentals of Power BI, SQL, and Python. • Created some tasks related to the lessons for students to exercise what they learned. • Created projects to be submitted by the student at the end of each course. • Set up a remote drive where all lessons note, resources, dataset, task, and projects are easily accessible by the student. • Guided the student on how to build a portfolio (LinkedIn and GitHub)

* Investigated a dataset to answer questions associated with it using the pandas' library.* Wrangled three datasets to make them cleaned for analysis using python.* Communicated data findings

* Performed a technical site survey for a small scale solar project.* Use a technical site survey to design a small scale solar system.* Connected a solar system both in series and parallel to power a 100 Watt load independently.