Cancer Dataset Tester

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This project is a simple implementation of Scikit-learn using pyscript and html

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How to load your own dataset

pass your own dataset into the cancer variable as shown below

cancer=datasets.load_breast_cancer()

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Explanation on how it works

• A simple random number generator is used to calculate a random state for splitting the data into test and train segments

  from sklearn import datasets 
  from sklearn.model_selection import train_test_split
  cancer=datasets.load_breast_cancer()
  print("Labels:", cancer.target_names)
  import random 
  randomness=random.randrange(0,120)
  print(f"Random state: {randomness}")
  X_train, X_test, y_train, true_test = train_test_split(cancer.data, cancer.target,test_size=0.3,random_state=randomness)

• A model is trained using scikit-learn's SVM and a prediction is ran

  from sklearn import svm 
  classifier= svm.SVC(kernel='linear')
  classifier.fit(X_train,y_train)
  predictor=classifier.predict(X_test)
  print(predictor)

• The model's accuracy is tested with Scikit learn's accuracy check function

      from sklearn import metrics
       accuracy=metrics.accuracy_score(true_test, predictor)
       rounded_accuracy=round(accuracy,2)
       print(f"Accuracy:{rounded_accuracy}%")
       if accuracy>highest_accuracy[0]:
           highest_accuracy=[accuracy,randomness]
    print(f"The Highest accuracy achieved was {round(highest_accuracy[0],2)}% using random state={highest_accuracy[1]}")

• The best result is stored and displayed

  if accuracy>highest_accuracy[0]:
                    highest_accuracy=[accuracy,randomness]
            print(f"The Highest accuracy achieved was {round(highest_accuracy[0],2)}% using random state={highest_accuracy[1]}")

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Feel free to reach out to me if you have futher questions! -Sankie