About Me
I am a data scientist with a background in finance.
Over the past year, I've embarked on an exciting journey into the world of data science, where I've dived deep into the nuances of cleaning, analyzing, and visualizing data, all the while perfecting the art of translating my discoveries into clear and compelling insights
During this immersive period, I enthusiastically explored the fascinating realm of machine learning, tackling real-world challenges by crafting Python-based algorithms for regression, classification, and unsupervised learning. This allowed me to transform raw data into practical solutions.
Leveraging my background in financial accounting, I seamlessly melded my analytical skills with the world of data science, weaving a unique blend that consistently produces outstanding results. I'm driven by my passion for bringing together the worlds of data and finance, constantly seeking innovative ways to make a meaningful impact.
Background
Explore AI Academy
-Data Science Student, 2023
-Intern, 2023
Intern, 2023
UNISA
BA Accounting Sciences Student, expected completion 2024.
Expertise
- Power BI
- Data Science Fundamentals
- Machine Learning
- Financial Analytics
- Proficient in SQL
- Proficient in Python
Projects
Climate Change Belief Classification
WebApp DEMO
Overview:
In collaboration with a team, I developed a machine learning model to classify Twitter users' beliefs regarding climate change. This project aimed to assist companies in understanding public sentiment towards climate change, thereby informing their marketing strategies for environmentally friendly products and services.
Approach
2
Data Preprocessing
1
Data Collection
3
Feature Engineering
5
Model Evaluation
6
Deployment
4
Model Building
Through this project, we demonstrated the potential of machine learning in understanding and analyzing social media data. Our work not only contributes to market research efforts but also highlights the importance of leveraging technology to address pressing global challenges such as climate change.
Movie Recommender System
Overview:
In collaboration with a team, I contributed to the development of a movie recommendation system aimed at assisting users in discovering relevant movie titles based on their preferences. This project addressed the growing need for intelligent recommender systems in today's technology-driven world.
Our goal was to construct a recommendation algorithm capable of accurately predicting how a user would rate a movie they had not yet viewed, leveraging their historical preferences. This involved implementing a content-based filtering approach to suggest movies that are similar in content to those the user has enjoyed in the past.
The resulting recommendation system effectively provided personalized movie suggestions to users based on their historical preferences. By leveraging content-based filtering techniques, the system accurately predicted how users would rate unseen movies, enhancing their movie-watching experience.
Approach
2
Data Preprocessing
1
Data Collection
3
Feature Engineering
5
Model Evaluation
6
Deployment
4
Model Building
WebApp Walkthrough
Transform your business with my dynamic blend of finance, data science, and creative brilliance.
