人工智能一百天.zip

import numpy as np import matplotlib.pyplot as plt import pandas as pd #导入数据集 dataset=pd.read_csv('Social_Network_Ads.csv') X=dataset.iloc[:,[2,3]].values Y=dataset.iloc[:,4].values from sklearn.model_selection import train_test_split X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0) #特征缩放 from sklearn.preprocessing import StandardScaler sc=StandardScaler() X_train=sc.fit_transform(X_train) X_test=sc.transform(X_test) #逻辑回归用于训练 from sklearn.linear_model import LogisticRegression classifier =LogisticRegression() classifier.fit(X_train,Y_train) Y_pred=classifier.predict(X_test) from sklearn.metrics import confusion_matrix cm=confusion_matrix(Y_test,Y_pred) #可视化 from matplotlib.colors import ListedColormap X_set,y_set=X_train,Y_train X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01), np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01)) plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(),X1.max()) plt.ylim(X2.min(),X2.max()) for i,j in enumerate(np. unique(y_set)): plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1], c = ListedColormap(('red', 'green'))(i), label=j) plt. title(' LOGISTIC(Training set)') plt. xlabel(' Age') plt. ylabel(' Estimated Salary') plt. legend() plt. show() X_set,y_set=X_test,Y_test X1,X2=np. meshgrid(np. arange(start=X_set[:,0].min()-1, stop=X_set[:, 0].max()+1, step=0.01), np. arange(start=X_set[:,1].min()-1, stop=X_set[:,1].max()+1, step=0.01)) plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(('red', 'green'))) plt.xlim(X1.min(),X1.max()) plt.ylim(X2.min(),X2.max()) for i,j in enumerate(np. unique(y_set)): plt.scatter(X_set[y_set==j,0],X_set[y_set==j,1], c = ListedColormap(('red', 'green'))(i), label=j) plt. title(' LOGISTIC(Test set)') plt. xlabel(' Age') plt. ylabel(' Estimated Salary') plt. legend() plt. show()