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Author: friea

knn_example.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jun 13 22:19:53 2020
+
+@author: HP
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split,GridSearchCV,cross_val_score
+from sklearn.metrics import mean_squared_error,r2_score
+from sklearn.neighbors import KNeighborsRegressor
+from warnings import filterwarnings
+import matplotlib.pyplot as plt
+xor=pd.read_csv("original.csv")
+df=xor.copy()
+df=df.drop(df.columns[0],axis=1)
+y = df["sales"]
+x = df.drop(["sales"],axis=1)
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=45)
+knn_model=KNeighborsRegressor().fit(x_train,y_train)
+knn_params={'n_neighbors': np.arange(1,135,1)}
+knn=KNeighborsRegressor()
+knn_cv_model = GridSearchCV(knn,knn_params,cv=10)
+knn_cv_model.fit(x_train,y_train)
+knn_tuned=KNeighborsRegressor(n_neighbors=knn_cv_model.best_params_["n_neighbors"])
+knn_tuned.fit(x_train,y_train)
+drmse0=np.sqrt(-1*cross_val_score(knn_model,x_test,y_test,cv=10,scoring="neg_mean_squared_error")).mean()
+drmse1=np.sqrt(-1*cross_val_score(knn_tuned,x_test,y_test,cv=10,scoring="neg_mean_squared_error")).mean()
+rmse0=np.sqrt(mean_squared_error(y_test,knn_model.predict(x_test)))
+rmse1=np.sqrt(mean_squared_error(y_test,knn_tuned.predict(x_test)))
+print("test hatası:"+str(("%.3f\n")%rmse0)+" doğrulanmış test hatası:"+str(("%.3f\n")%drmse0))
+print("model tunning sonrası test hatası:"+str(("%.3f\n")%rmse1)+" model tunning sonrası doğrulanmış test hatası:"+str(("%.3f")%drmse1))
+tv=input("TV reklam sayısı: ")
+radyo=input("radyo reklam sayısı: ")
+gazete=input("gazete reklam sayısı: ")
+veri=[[tv,radyo,gazete]]
+filterwarnings('ignore')
+print("\n tahmini satış: "+str(("%d")%knn_tuned.predict(veri)))
+r2=r2_score(y_test,knn_tuned.predict(x_test))
+
+
+
+
+
+
+

knn_xor.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jun 14 05:53:27 2020
+
+@author: HP
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import GridSearchCV,cross_val_score
+from sklearn.metrics import mean_squared_error,r2_score
+from sklearn.neighbors import KNeighborsRegressor
+from warnings import filterwarnings
+xor=pd.read_excel("xor.xlsx")
+df=xor.copy()
+y = df["y"]
+x = df.drop(["y"],axis=1)
+knn_model=KNeighborsRegressor(n_neighbors=2).fit(x,y)
+knn_params={'n_neighbors': np.arange(1,4,1)}
+knn=KNeighborsRegressor()
+knn_cv_model = GridSearchCV(knn,knn_params,cv=3)
+knn_cv_model.fit(x,y)
+knn_tuned=KNeighborsRegressor(n_neighbors=knn_cv_model.best_params_["n_neighbors"])
+knn_tuned.fit(x,y)
+drmse0=np.sqrt(-1*cross_val_score(knn_model,x,y,cv=2,scoring="neg_mean_squared_error")).mean()
+drmse1=np.sqrt(-1*cross_val_score(knn_tuned,x,y,cv=2,scoring="neg_mean_squared_error")).mean()
+rmse0=np.sqrt(mean_squared_error(y,knn_model.predict(x)))
+rmse1=np.sqrt(mean_squared_error(y,knn_tuned.predict(x)))
+print("test hatası:"+str(("%.3f\n")%rmse0)+" doğrulanmış test hatası:"+str(("%.3f\n")%drmse0))
+print("model tunning sonrası test hatası:"+str(("%.3f\n")%rmse1)+" model tunning sonrası doğrulanmış test hatası:"+str(("%.3f")%drmse1))
+filterwarnings('ignore')
+r2=r2_score(y,knn_tuned.predict(x))
+x1=input("ikilik tabanda birinci sayı")
+x2=input("ikilik tabanda ikinci sayı")
+yeni_veri=[[x1,x2]]
+print("tahmin edilen y değeri: "+ str(("%d")%knn_tuned.predict(yeni_veri)))