Python

Using the datafile cancer.csv

Develop a model with $2$ of SVC$,$ Random Forrest, and KNN that performs a binary classification prediction of the target variable: malignant or benign

Perform EDA

Perform Data Preparation including dealing with categorical data and missing data, normalization, binning if appropriate, feature reduction or selection strategies if helpful. Feature engineering can also be attempted if you feel it aids in your analysis.

Make sure to divide data into testing and training sets.

Train and test your model $-$ use hyperparameter tuning methods $($ex$.$ gridsearch; be careful to not use too many options$)$ and pipelines $($even if just for the model initialization$)$

Produce effectiveness metrics and results. Discuss which are most relevant and analyze results. Does your approach generalize? Why are the metrics you chose to assess performance relevant?

Bypass iteration this time but compare the different models' results and provide commentary.

explain your approach and provide conclusion