This repository contains code for training and evaluating classification models for breast cancer detection tasks.
- Python 3
- Jupyter Notebook
- scikit-learn
- Matplotlib (for visualization, if needed)
You can install the required packages using pip:
pip install scikit-learn numpy matplotlib jupyter
- Clone this repository:
git clone https://github.com/nikithamarythomas/AIDI2004.git
Main branch : Logistic Regression
Branch 'branch' : SVM
Launch the notebook in your preferred IDE to use the models.
Both models are trained and evaluated on the Breast Cancer Wisconsin (Diagnostic) Dataset, available in scikit-learn's datasets module. The goal to predict whether the mass is benign or malignant.
When working with Jupyter Notebook files (.ipynb), you can use nbdiff to visualize Git diffs more effectively.
nbdiff is a tool that provides a rich visualization of differences between Jupyter Notebooks. You can install it via pip:
pip install nbdime
Integrate nbdiff with git:
nbdime config-git --enable
After configuration, you can use git diff as usual, and nbdime diff will provide a human-readable diff: