- 👋 Hi, I’m Darren @ags911
- 👀 I’m interested in Football, Gaming, Music, Aviation and Data Science.
- 🌱 I’m currently learning how to fly.
- 💞️ I’m looking to collaborate on interesting Data Science and other coding projects.
- 📫 How to reach me... https://www.linkedin.com/in/darren-gidado/
- 🌍 Check out my blog / portfolio here... https://darrengidado.com/
Project 1: Write A Data Science Blog Post
Airbnb empowers homeowners, driving a surge in investors acquiring properties specifically for rentals. Success hinges on understanding guest appeal: location, amenities, unique features, competitive pricing, and effective marketing. By carefully considering these aspects, Airbnb hosts can optimize their properties for guest satisfaction and profitability.
Project 2: Disaster Response Pipeline Project
This project analyzed disaster data from Figure Eight using data engineering skills to build a Machine Learning pipeline to categorize these events so that the messages could be sent to an appropriate disaster relief agency. A web app allows emergency workers to input messages for rapid classification, directing aid efficiently.
Project 3: IBM Recomendation Engine Project
This project, a collaboration with IBM, focuses on developing a recommendation engine for the IBM Watson Community. By exploring various recommendation techniques, such as collaborative filtering and content-based filtering, we aim to suggest relevant articles to users, enhancing their overall experience on the platform and fostering greater engagement within the IBM Watson Community.
Project 4: Starbucks Capstone Project
This project uses simulated data to provide insight to how people make purchasing decisions, and how those decisions are influenced by promotional offers. The data contains various events, including receiving offers, opening offers, and making purchases. Our task is to identify which groups of people are most responsive to each type of offer by finding traits and purchasing patterns.
Intro Project: Explore Weather Trends
The Exploring Weather Trends project demonstrates using SQL to download and analyze data from a database. We will compare local and global temperature trends by analyzing this data, showcasing our ability to manage and interpret large datasets. This project aims to uncover significant patterns and differences in temperature changes.
Project 1: Investigating TMDB Movies Dataset
In this project, we'll select one of Udacity's curated datasets and use NumPy and pandas to investigate it. We'll follow the entire data analysis process, from posing a question to sharing findings. This approach showcases our ability to handle, analyze, and interpret data, and demonstrates our proficiency in using powerful data science tools.
Project 2: Analyze Experiment Results
In this project, we will be provided a dataset reflecting data collected from an landing page experiment. We’ll use statistical techniques to answer questions about the data and report our conclusions and recommendations. We will also use machine learning and logistic regression to predict how various factors impact conversions on the landing page.
Project 3: We Rate Dogs Twitter Analysis
Real-world data rarely comes clean. Using Python, we’ll gather data from a variety of sources, assess its quality and tidiness, then clean it. Our wrangling efforts will be documented in a Jupyter Notebook. Additionally, we will showcase our work through analyses and visualizations using Python and SQL, highlighting our data handling skills.
Project 4: The Truth About Airline Statistics
This dataset reports flights in the United States from 1987 to 2008 including arrival and departure delays, carriers, and other data. We will be sampling the three year period between 89' to 91' as the whole dataset is too large to practically observe with Jupyter. Findings will be visualised in univariate, bivariate and, multivariate plots.