This repository documents my journey while learning machine learning and data processing in Python. It includes everything from cleaning raw datasets to building predictive models and applying those skills to real research projects.
Each folder showcases a different phase of learning, from understanding messy data to creating end-to-end machine learning applications.
Contains Jupyter notebooks and scripts focused on data cleaning and preprocessing, including handling missing values, removing outliers, normalizing datasets, and preparing data for analysis.
Focus: Understanding how to clean and structure real-world data before modeling.
Includes different machine learning models and experiments, such as:
- Binary Classification
- Decision Trees
- Linear Regression
- Linear Classification
- Image Recognition
Also features mini-projects like:
- 🏠 House Price Prediction – simple regression-based model
- 🚗 Lane Detection – computer vision basics
- 📈 Tesla Stock Prediction (WIP) – using linear regression for trend analysis
Focus: Learning how different ML algorithms work and applying them to small projects.
A project inspired by research conducted at UIUC for POTS (Postural Orthostatic Tachycardia Syndrome) patients.
This folder focuses on data cleaning and visualization, making complex and lengthy data outputs easier to read, analyze, and download.
- Cleaned raw experimental data
- Applied formatting and structure for scientific readability
- Exported organized datasets for use by researchers
Focus: Using data science to make real-world medical research more accessible and interpretable.
- Languages: Python
- Libraries: pandas, numpy, matplotlib, scikit-learn, seaborn, OpenCV
- Application: PyCharm