README

This is my project submission. I’m Andrew Farell, and the materials in this repository pertain to the Histopathologic Cancer Detection competition on Kaggle.

Directory Structure

.
├── cancer.ipynb                  # Main Jupyter Notebook for data exploration and modeling
├── histopathologic-cancer-detection
│   ├── sample_submission.csv     # Template for Kaggle submissions
│   ├── test                      # Folder with unlabeled test images
│   ├── train                     # Folder with labeled training images
│   └── train_labels.csv          # CSV containing ground-truth labels for train images
└── my_submission.csv             # Example output file for final test predictions

Downloading the Dataset

1. Sign in to Kaggle
   If you haven’t already, create an account at Kaggle.

2. Install Kaggle CLI (if using command line)

   pip install kaggle

   Make sure your Kaggle API credentials are set up in ~/.kaggle/kaggle.json.

3. Access the Competition
   Go to the Histopathologic Cancer Detection competition page.

4. Download the Data

   • From the competition’s Data section, click Download All
     or
   • Use the CLI:

     kaggle competitions download -c histopathologic-cancer-detection

5. Unzip the File

   unzip histopathologic-cancer-detection.zip -d histopathologic-cancer-detection

   You should see train/, test/, and CSV files inside the histopathologic-cancer-detection/ folder.

With the dataset prepared, you can run cancer.ipynb or any additional scripts to train and evaluate your models. All final predictions for submission to Kaggle should follow the format in my_submission.csv.