鲍鱼年龄预测 knn svm 逻辑回归有代码可运行

本文链接：https://blog.youkuaiyun.com/pythonyanyan/article/details/128392859

数据：

M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
M,0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10
I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
I,0.425,0.3,0.095,0.3515,0.141,0.0775,0.12,8
F,0.53,0.415,0.15,0.7775,0.237,0.1415,0.33,20
F,0.545,0.425,0.125,0.768,0.294,0.1495,0.26,16
M,0.475,0.37,0.125,0.5095,0.2165,0.1125,0.165,9
F,0.55,0.44,0.15,0.8945,0.3145,0.151,0.32,19
F,0.525,0.38,0.14,0.6065,0.194,0.1475,0.21,14
M,0.43,0.35,0.11,0.406,0.1675,0.081,0.135,10
M,0.49,0.38,0.135,0.5415,0.2175,0.095,0.19,11
F,0.535,0.405,0.145,0.6845,0.2725,0.171,0.205,10
F,0.47,0.355,0.1,0.4755,0.1675,0.0805,0.185,10
M,0.5,0.4,0.13,0.6645,0.258,0.133,0.24,12
I,0.355,0.28,0.085,0.2905,0.095,0.0395,0.115,7
F,0.44,0.34,0.1,0.451,0.188,0.087,0.13,10
M,0.365,0.295,0.08,0.2555,0.097,0.043,0.1,7
M,0.45,0.32,0.1,0.381,0.1705,0.075,0.115,9
M,0.355,0.28,0.095,0.2455,0.0955,0.062,0.075,11
I,0.38,0.275,0.1,0.2255,0.08,0.049,0.085,10
F,0.565,0.44,0.155,0.9395,0.4275,0.214,0.27,12
F,0.55,0.415,0.135,0.7635,0.318,0.21,0.2,9
F,0.615,0.48,0.165,1.1615,0.513,0.301,0.305,10

代码：

import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error,mean_absolute_error  # 评价指标
from sklearn.linear_model import LogisticRegression
data_x=[]
data_y=[]
with open("abalone.data",encoding="utf-8") as f :
    f=f.readlines()
    for line in f:
        line=line.split("\n")[:-1][0].split(",")[1:]
        line=[float(i) for i in line]
        # print(line)
        data_x.append(line[:-1])
        data_y.append(line[-1])
# 对样本进行随意切割，得到训练集和验证集
x_train,x_test,y_train,y_test = train_test_split(np.array(data_x),np.array(data_y),test_size=0.2)

# knn算法 
knn = KNeighborsRegressor()
knn.fit(x_train, y_train)
test_pred = knn.predict(x_test)  # 进行预测
print("knn算法 ")
print(test_pred[:10])
print(y_test[:10])
print('mean_squared_error:', mean_squared_error(y_test, test_pred,sample_weight=None, multioutput='uniform_average'))# mse
print("mean_absolute_error:",mean_absolute_error(y_test, test_pred,sample_weight=None,multioutput='uniform_average'))# mae
# svm算法 
svm = SVC()
svm.fit(x_train, y_train)
test_pred = svm.predict(x_test)
print("svm算法 ")
print(test_pred[:10])
print(y_test[:10])
print('mean_squared_error:', mean_squared_error(y_test, test_pred,sample_weight=None, multioutput='uniform_average'))# mse
print("mean_absolute_error:",mean_absolute_error(y_test, test_pred,sample_weight=None,multioutput='uniform_average'))# mae
# 逻辑回归算法
lr = LogisticRegression()
lr.fit(x_train, y_train)
test_pred = lr.predict(x_test)
print("逻辑回归算法 ")
print(test_pred[:10])
print(y_test[:10])
print('mean_squared_error:', mean_squared_error(y_test, test_pred,sample_weight=None, multioutput='uniform_average'))# mse
print("mean_absolute_error:",mean_absolute_error(y_test, test_pred,sample_weight=None,multioutput='uniform_average'))# mae