Stock Market Analysis, EDA, Linear Regression
This project encompasses a thorough analysis of global stock markets. The initial phase entailed conducting an exploratory data analysis (EDA) to glean insights from the datasets. Subsequently, I developed a robust linear regression model, necessitating meticulous feature engineering. The overarching objective was not only to gain a deep understanding of diverse stock markets worldwide but also to embark on pattern prediction endeavors. This involved the identification and utilization of predictive insights within the data, with the aim of enhancing decision-making processes.
The overarching objective of this analysis was to assist the marketing team in their pursuit of expanding their business. As part of my analysis of the given dataset, I conducted an exploratory data analysis (EDA) to thoroughly understand its characteristics. Upon careful examination, it became evident that employing the K-means clustering algorithm was the most suitable approach for the task at hand. By leveraging K-means clustering, I successfully identified distinct clusters within the data, providing a foundation for the marketing team to streamline their efforts.
A similar analysis of a given dataset, I conducted an exploratory data analysis (EDA) to gain a comprehensive understanding of its characteristics. Again the primary objective was to support the marketing team's expansion efforts. As part of this endeavor, I employed various data analysis techniques, including Principal Component Analysis (PCA) for dimensionality reduction, in addition to K-means and hierarchial clustering. These methods collectively provided valuable insights into the data's structure and patterns. These clusters serve as a foundational tool for the marketing team, empowering them to tailor their strategies to each cluster's unique characteristics. This multifaceted approach not only supports the business expansion goals but also enhances the data-driven decision-making processes.
A web application to crowdsource the price of a coin, aiming to assist numismatists. The project features a backend built with various AWS services, including Lambda functions, DynamoDB, Amplify, API Gateway, CloudFront, CloudWatch, Route 53, and more, while the frontend is developed using HTML, CSS, and JavaScript.
My public Tableau profile showcases a diverse range of data visualizations, each designed to provide valuable insights. Some of the featured visualizations include crime statistics for the City of Cincinnati, a comprehensive dashboard displaying Netflix's movie catalog, an analysis of IMDB movie ratings, and several others. These visualizations serve as examples of my proficiency in data analytics and visualization techniques, demonstrating my ability to translate complex data into meaningful and informative graphics.
You can find my public GitHub profile, which showcases a collection of machine learning projects, ETL (Extract, Transform, Load) examples, and more. These repositories serve as a testament to my expertise in data science, machine learning, and software development.
I have developed an AWS-based application that leverages AlphaVantage's API to retrieve real-time stock market data. Specifically, the application monitors the S&P 500 index and detects significant daily movements, signaling notifications when the index moves by more than ±1%.