These are just an overview of my projects. For further details, kindly click the hyperlinked header of each project to assess my GitHub (Project 4 is currently not available on my GitHub as it contains confidential enterprise data).
- Made predictions about life expectancy of a person based on the Happiness Indicators using some ML algorithms.
- Pre-processed multiple data sets retrieved from Kaggle using Pandas and NumPy.
- Presented descriptive statistics with improved visuals using SweetViz visualyzer.
- Performed visualisations and EDA using Seaborn and Matplotlib.
- Performed predictions using 5 ML models with Scikit learn.
- Created an interactive dashboard using IBM employee data set from Kaggle.
- Guided by coursemates with programming experience with constructive feedbacks.
- Made reactive visualisations that react upon input changes.
- Ensured simple yet visually pleasing UI for users' ease of navigating through the dashboard.
- Similar to Project 1 except that it was done in R with different ML algorithms used on a smaller data set.
- Followed the data science workflow, CRISP-DM for the completion of the project.
- Used RapidMiner in addition to R's ML packages to perform quick ML modelling on the data set.
- Participated in a small-scaled "Data Challenge" hosted by a Singaporean training academy.
- Participants were given actual F&B data set to derive meaningful insights to be presented to the panel of judges by the end of September 2022.
- Pre-processed the enterprise data set with Python using Pandas and Numpy.
- Performed EDA on the enterprise data set using Tableau.
- Considering the practicality if real value can be generated from leveraging ML on these data.
