Additional Information
Linkendin Profile: https://www.linkedin.com/in/sunday-okechukwu-msc-747485184/
Github Profile: https://github.com/
Medium Profile: https://medium.com/p/c670ce484a30
Projects
Classification of Disaster Messages: https://medium.com/p/c670ce484a30
- In this project, I applied supervised machine learning techniques and analytical mind to help classify disaster messages.
- First and foremost, I explored the messages and categories data to learn how they were recorded. Next, I applied a series of transformations and preprocessing techniques to manipulate the data into a workable format.
- I then evaluated several Supervised learners of my choice (Support Vector Machine, Decision Trees, Random Forest Trees and AdaBoost) on the data and considered which is best suited for the solution.
- Afterwards, I optimized the model I selected using the GridSearchCV. Finally, I explored the chosen model and its prediction under the hood to see how well it is performing.
Analyzed Bike Share Systems in the US: https://github.com/Sunday-Okey/Bike-Share-Project
- I used Python to explore data related to bike share systems for three major cities in the United States — Chicago, New York City, and Washington.
- I wrote code to import the data and answer interesting questions about it by computing descriptive statistics.
- Finally, I also wrote a script that takes in raw input to create an interactive experience in the terminal to present these statistics to the user audience.
Wrangle and Analyze Data: https://github.com/Sunday-Okey/Udacity_Data_Wrangling_Project
- Gathered data from a variety of sources and in a variety of formats (TSV, CSV, JSON, flat files etc) including Tweet API JSON data using Python Tweepy library and assessed the data for quality and tidiness issues.
- Merged and cleaned the master data, stored the dataframes into csv and visualized the data using Python libraries.