🦠 Cancer Data Classification With K-NN

🏆 Test accuracy: 95.32% (3 nn)

🔗 Kaggle Link

You can access the detailed description of the project from this link https://www.kaggle.com/code/erdemtaha/prediction-cancer-data-with-k-nn-95

📑 Project Summary

Using the Knn algorithm, we analyze and classify the cancer cells we have as benign or malignant

1 = M (Malignant Cancer Cell)

0 = B (Benign Cancer Cell)

⚙️ Data information

My data consists of 569 cancer cells and 30 characteristics of each cell.

📈 Plot of Find K Values

from sklearn.neighbors import KNeighborsClassifier
Score_list = []

for each in range(1,15):
    knn2 = KNeighborsClassifier(n_neighbors = each)
    knn2.fit(x_train,y_train)
    Score_list.append(knn2.score(x_test,y_test))
    
plt.plot(range(1,15),Score_list)
plt.xlabel("k values")
plt.ylabel("accuracy")
plt.show()

🤖 K-NN Model

Here we examine the accuracy score of the K-nn Model and we get a score of 0.953216373742690059 which indicates that we have trained a good model in general, of course, better results can be obtained by playing with the values.

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(x_train,y_train)
prediction = knn.predict(x_test)
print("{} nn {} score".format(3,knn.score(x_test,y_test)))

Result

🏆 Test accuracy: 95.32% (3 nn)