Welcome to my data science journey into all things machine learning. This is were I explore, iterate and play around with new concepts and models as part of my super intense data science bootcamp!
- Each folder is a mini project exploring different machine learning problems. From house pricing with regression to credit risk models with random forests.
- Jupyter notebooks containing the code for data cleaning, exploration, analysis, model building, and evaluation.
- Supporting files (if any) such as datasets.
The specific setup will vary depending on the project's requirements. However, generally you'll need:
- Python (version 3.7.0 or higher) - https://www.python.org/downloads/
- Jupyter Notebook - https://jupyter.org/install
- Data science libraries like pandas, NumPy, scikit-learn, etc. These can be installed using pip: pip install 'library_name'
This repo is of my own learning journey, but feel free to peek around, explore the code, and maybe even give me some pointers! If you have any ideas or see ways to improve things,have a more efficient way of doing things, let me know!
PS: The repo is still work in progress, so I will be including more projects as I advance in the bootcamp.