knn.py

# from sklearn.datasets import load_breast_cancer
# from sklearn.neighbors import KNeighborsClassifier
# from sklearn.metrics import accuracy_score , confusion_matrix
# from sklearn.model_selection import train_test_split
# from sklearn.preprocessing  import StandardScaler
# cancer = load_breast_cancer()
# import pandas as pd
# import matplotlib.pyplot as plt 

# df= pd.DataFrame(data=cancer.data,columns=cancer.feature_names)
# df["target"]=cancer.target

# X = cancer.data
# y= cancer.target

# X_train , X_test, y_train , y_test = train_test_split(X,y,test_size=0.3,random_state=42)

# scaler=StandardScaler()
# X_train= scaler.fit_transform(X_train)
# X_test= scaler.transform(X_test)


# knn = KNeighborsClassifier(n_neighbors=3)
# knn.fit(X_train,y_train)

# y_pred = knn.predict(X_test)

# accuracy = accuracy_score(y_test,y_pred)
# print("dogruluk:" ,accuracy)

# conf_matrix = confusion_matrix(y_test,y_pred)
# print("confusion matrix" ,conf_matrix)

# accuracy_values=[]
# k_values=[]

# for k in range(1,21):
#     knn = KNeighborsClassifier(n_neighbors=k)
#     knn.fit(X_train,y_train)
#     y_pred= knn.predict(X_test)
#     accuracy= accuracy_score(y_test,y_pred)
#     accuracy_values.append(accuracy)
#     k_values.append(k)

# plt.figure()
# plt.plot(k_values,accuracy_values, marker ="o",linestyle="-")
# plt.title("k değerine göre doğruluk tablosu")
# plt.xlabel("k değeri")
# plt.ylabel("dogruluk")
# plt.xticks(k_values)
# plt.grid(True)
# plt.show()

# %%
import numpy as np 
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsRegressor

X = np.sort(5*np.random.rand(40,1),axis=0)
y= np.sin(X).ravel()

y[::5] += 1*(0.5 - np.random.rand(8))

# plt.scatter(X,y)
# plt.show()

T= np.linspace(0,5,500)[:,np.newaxis]

for i,weight in enumerate(["uniform","distance"]):
    knn= KNeighborsRegressor(n_neighbors=5,weights=weight)
    y_pred= knn.fit(X,y).predict(T)

    plt.subplot(2,1,i+1)
    plt.scatter(X,y,color="green",label="data")
    plt.plot(T,y_pred,color="blue",label="prediction")
    plt.axis("tight")
    plt.legend( )
plt.tight_layout()
plt.show()