Hello, I'm Jason! 👋 🎯 Data Scientist | Problem Solver | Machine Learning Enthusiast

Welcome to my GitHub! I'm a passionate data scientist with a deep curiosity for uncovering insights through data and creating solutions that drive real-world impact. My journey in data science has been focused on leveraging data-driven approaches to solve complex problems, build predictive models, and collaborate across teams to generate value.

🚀 What I Do: Data Analysis: Turning raw data into actionable insights. Machine Learning: Developing models that make accurate predictions and optimize business decisions. Data Wrangling: Cleaning and preparing datasets for analysis. Visualization: Communicating data insights effectively using tools like Matplotlib, Seaborn, and Tableau. Collaboration: Working closely with cross-functional teams to integrate data-driven solutions into broader strategies.

🛠️ My Toolset: Programming Languages: Python, R, SQL Machine Learning Libraries: scikit-learn, TensorFlow, Keras, XGBoost Data Analysis & Visualization: pandas, NumPy, Matplotlib, Seaborn, Plotly Databases: SQL, MongoDB, PostgreSQL Cloud & Big Data: AWS (S3, EC2), Google Cloud, Hadoop, Spark Version Control: Git, GitHub

🌟 What Drives Me: Curiosity: Constantly learning new techniques, algorithms, and tools to stay ahead in this ever-evolving field. Impact: Delivering solutions that not only perform well in models but also bring tangible value to businesses and users. Collaboration: I believe data science is best when done collaboratively, ensuring alignment between data and business goals. Problem-Solving: From messy data to complex models, I thrive on challenges and enjoy solving problems using data.

📝 My Projects: Here are a few projects that demonstrate my skills and interests:

Body Signal of Smoking Classifier: Using body signals from medical data, this is a binary classifier to detect smoking in humans. Techniques used range from Logistic Regression to Neural Network deployment.

Technologies: Python, scikit-learn, pandas, etc.

Secondary School Grade Predictor: Using data from schools and cognitive aptitude tests, data visualisation techniques used with matplotlib, seaborn. Supervised learning approach was taken here.

Technologies: matplotlib, seaborn, random forest, XGBoost, etc.

🤝 Let’s Connect: I'm always open to new opportunities, collaborations, and discussions around data science. If you're interested in connecting or learning more about my work, feel free to reach out!

LinkedIn: https://www.linkedin.com/in/jason-ronald/

Email: jasonronaldbsc@gmail.com

Thank you for visiting my GitHub! Looking forward to connecting and exploring the exciting world of data together!