cpreproc.mojo

# Inferences and Applications in Cancer Research
"""
Here is an initial example of how to build out an ML model or neural network
framework in PyMo. Sparing the details, data preprocessing is crucial in ML and DL
domains. This is an example where the framework can be utilized for applications
in cancer research and other domains in medicine.
"""
from python import Python


fn main() raises:
    var np = Python.import_module("numpy")
    var pd = Python.import_module("pandas")
    var plt = Python.import_module("matplotlib.pyplot")
    var math = Python.import_module("math")

    # Load and read datasets - update file paths as needed
    """ 
    Training and testing data to run inference, preprocess, and to 
    benchmark and run a model 
    """
    df = pd.read_csv("~/modular/pymo/data/cancer-data/training.csv")
    df2 = pd.read_csv("~/modular/pymo/data/cancer-data/test.csv")

    # Training dataset
    print("Training dataset:")
    print(df.head())
    print()

    # Testing dataset
    print("Testing dataset:")
    print(df2.head())