Python机器学习—BP神经网络模型预测西瓜好坏

import pandas as pd from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report ## 读取数据 data = pd.read_csv('data.csv', encoding='gbk') ## 将target变为数字 data.loc[data['好瓜与否']!= '是','好瓜与否'] = 0 data.loc[data['好瓜与否']== '是','好瓜与否'] = 1 data['好瓜与否'] = data['好瓜与否'].astype('int') ## 取出X和y X = pd.get_dummies(data.iloc[:,1:-1]).values y = data.iloc[:,-1].values ## 切割数据集 X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.8,random_state=125) ## 建模并预测 BPNet = MLPClassifier(random_state=123) BPNet.fit(X_train,y_train) y_pred = BPNet.predict(X_test) #print(y_test,y_pred) # #输出预测结果报告 print('预测报告为：\n',classification_report(y_test,y_pred))

西瓜数据集： 编号,色泽,根蒂,敲声,纹理,脐部,触感,密度,含糖率,好瓜与否 1,青绿,蜷缩,浊响,清晰,凹陷,硬滑,0.697,0.46,是 2,乌黑,蜷缩,沉闷,清晰,凹陷,硬滑,0.774,0.376,是 3,乌黑,蜷缩,浊响,清晰,凹陷,硬滑,0.634,0.264,是 4,青绿,蜷缩,沉闷,清晰,凹陷,硬滑,0.608,0.318,是 5,浅白,蜷缩,浊响,清晰,凹陷,硬滑,0.556,0.215,是 6,青绿,稍蜷,浊响,清晰,稍凹,软粘,0.403,0.237,是 7,乌黑,稍蜷,浊响,稍糊,稍凹,软粘,0.481,0.149,是 8,乌黑,稍蜷,浊响,清晰,稍凹,硬滑,0.437,0.211,是 9,乌黑,稍蜷,沉闷,稍糊,稍凹,硬滑,0.666,0.091,否 10,青绿,硬挺,清脆,清晰,平坦,软粘,0.243,0.267,否 11,浅白,硬挺,清脆,模糊,平坦,硬滑,0.245,0.057,否 12,浅白,蜷缩,浊响,模糊,平坦,软粘,0.343,0.099,否 13,青绿,稍蜷,浊响,稍糊,凹陷,硬滑,0.639,0.161,否 14,浅白,稍蜷,沉闷,稍糊,凹陷,硬滑,0.657,0.198,否 15,乌黑,稍蜷,浊响,清晰,稍凹,软粘,0.36,0.37,否 16,浅白,蜷缩,浊响,模糊,平坦,硬滑,0.593,0.042,否 17,青绿,蜷缩,沉闷,稍糊,稍凹,硬滑,0.719,0.103,否