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python成绩判断系统(Python根据成绩分析系统浅析)

更多 时间:2022-03-30 00:13:21 类别:脚本大全 浏览量:1967

python成绩判断系统

Python根据成绩分析系统浅析

案例:该数据集的是一个关于每个学生成绩的数据集,接下来我们对该数据集进行分析,判断学生是否适合继续深造

数据集特征展示

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  • 1  gre 成绩 (290 to 340)
  • 2  toefl 成绩(92 to 120)
  • 3  学校等级 (1 to 5)
  • 4  自身的意愿 (1 to 5)
  • 5  推荐信的力度 (1 to 5)
  • 6  cgpa成绩 (6.8 to 9.92)
  • 7  是否有研习经验 (0 or 1)
  • 8  读硕士的意向 (0.34 to 0.97)
  • 1.导入包

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  • import numpy as np
  • import pandas as pd
  • import matplotlib.pyplot as plt
  • import seaborn as sns
  • import os,sys
  • 2.导入并查看数据集

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  • df = pd.read_csv("d:\\machine-learning\\score\\admission_predict.csv",sep = ",")<br>print('there are ',len(df.columns),'columns')<br>for c in df.columns:<br> sys.stdout.write(str(c)+', '
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  • there are 9 columns
  • serial no., gre score, toefl score, university rating, sop, lor , cgpa, research, chance of admit , <br>一共有9列特征
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  • df.info()
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  • <class 'pandas.core.frame.dataframe'>
  • rangeindex: 400 entries, 0 to 399
  • data columns (total 9 columns):
  • serial no.   400 non-null int64
  • gre score   400 non-null int64
  • toefl score   400 non-null int64
  • university rating 400 non-null int64
  • sop     400 non-null float64
  • lor     400 non-null float64
  • cgpa     400 non-null float64
  • research    400 non-null int64
  • chance of admit  400 non-null float64
  • dtypes: float64(4), int64(5)
  • memory usage: 28.2 kb<br><br>数据集信息:<br>1.数据有9个特征,分别是学号,gre分数,托福分数,学校等级,sop,lor,cgpa,是否参加研习,进修的几率<br>2.数据集中没有空值<br>3.一共有400条数据
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  • # 整理列名称
  • df = df.rename(columns={'chance of admit ':'chance of admit'})<br># 显示前5列数据<br>df.head()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    3.查看每个特征的相关性

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  • fig,ax = plt.subplots(figsize=(10,10))
  • sns.heatmap(df.corr(),ax=ax,annot=true,linewidths=0.05,fmt='.2f',cmap='magma')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.最有可能影响是否读硕士的特征是gre,cgpa,toefl成绩

    2.影响相对较小的特征是lor,sop,和research

    4.数据可视化,双变量分析

    4.1 进行research的人数

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  • print("not having research:",len(df[df.research == 0]))
  • print("having research:",len(df[df.research == 1]))
  • y = np.array([len(df[df.research == 0]),len(df[df.research == 1])])
  • x = np.arange(2)
  • plt.bar(x,y)
  • plt.title("research experience")
  • plt.xlabel("canditates")
  • plt.ylabel("frequency")
  • plt.xticks(x,('not having research','having research'))
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

      结论:进行research的人数是219,本科没有research人数是181

      4.2 学生的托福成绩

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  • y = np.array([df['toefl score'].min(),df['toefl score'].mean(),df['toefl score'].max()])
  • x = np.arange(3)
  • plt.bar(x,y)
  • plt.title('toefl score')
  • plt.xlabel('level')
  • plt.ylabel('toefl score')
  • plt.xticks(x,('worst','average','best'))
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:最低分92分,最高分满分,进修学生的英语成绩很不错

    4.3 gre成绩

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  • df['gre score'].plot(kind='hist',bins=200,figsize=(6,6))
  • plt.title('gre score')
  • plt.xlabel('gre score')
  • plt.ylabel('frequency')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:310和330的分值的学生居多

    4.4 cgpa和学校等级的关系

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  • plt.scatter(df['university rating'],df['cgpa'])
  • plt.title('cgpa scores for university ratings')
  • plt.xlabel('university rating')
  • plt.ylabel('cgpa')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:学校越好,学生的gpa可能就越高

    4.5 gre成绩和cgpa的关系

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  • plt.scatter(df['gre score'],df['cgpa'])
  • plt.title('cgpa for gre scores')
  • plt.xlabel('gre score')
  • plt.ylabel('cgpa')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:gpa基点越高,gre分数越高,2者的相关性很大

    4.6 托福成绩和gre成绩的关系

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  • df[df['cgpa']>=8.5].plot(kind='scatter',x='gre score',y='toefl score',color='red')
  • plt.xlabel('gre score')
  • plt.ylabel('toefl score')
  • plt.title('cgpa >= 8.5')
  • plt.grid(true)
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:多数情况下gre和托福成正相关,但是gre分数高,托福一定高。

    4.6 学校等级和是否读硕士的关系

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  • s = df[df['chance of admit'] >= 0.75]['university rating'].value_counts().head(5)
  • plt.title('university ratings of candidates with an 75% acceptance chance')
  • s.plot(kind='bar',figsize=(20,10),cmap='pastel1')
  • plt.xlabel('university rating')
  • plt.ylabel('candidates')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:排名靠前的学校的学生,进修的可能性更大

    4.7 sop和gpa的关系

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  • plt.scatter(df['cgpa'],df['sop'])
  • plt.xlabel('cgpa')
  • plt.ylabel('sop')
  • plt.title('sop for cgpa')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论: gpa很高的学生,选择读硕士的自我意愿更强烈

    4.8 sop和gre的关系

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  • plt.scatter(df['gre score'],df['sop'])
  • plt.xlabel('gre score')
  • plt.ylabel('sop')
  • plt.title('sop for gre score')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:读硕士意愿强的学生,gre分数较高

    5.模型

    5.1 准备数据集

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  • # 读取数据集
  • df = pd.read_csv('d:\\machine-learning\\score\\admission_predict.csv',sep=',')
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  • serialno = df['serial no.'].values
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  • df.drop(['serial no.'],axis=1,inplace=true)
  • df = df.rename(columns={'chance of admit ':'chance of admit'})
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  • # 分割数据集
  • y = df['chance of admit'].values
  • x = df.drop(['chance of admit'],axis=1)
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  • 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=42)
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  • # 归一化数据<br>from sklearn.preprocessing import minmaxscaler<br>scalex = minmaxscaler(feature_range=[0,1])<br>x_train[x_train.columns] = scalex.fit_transform(x_train[x_train.columns])<br>x_test[x_test.columns] = scalex.fit_transform(x_test[x_test.columns])
  • 5.2 回归

    5.2.1 线性回归

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  • from sklearn.linear_model import linearregression
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  • lr = linearregression()
  • lr.fit(x_train,y_train)
  • y_head_lr = lr.predict(x_test)
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  • print('real value of y_test[1]: '+str(y_test[1]) + ' -> predict value: ' + str(lr.predict(x_test.iloc[[1],:])))
  • print('real value of y_test[2]: '+str(y_test[2]) + ' -> predict value: ' + str(lr.predict(x_test.iloc[[2],:])))
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  • from sklearn.metrics import r2_score
  • print('r_square score: ',r2_score(y_test,y_head_lr))
  • y_head_lr_train = lr.predict(x_train)
  • print('r_square score(train data):',r2_score(y_train,y_head_lr_train))
  • python成绩判断系统(Python根据成绩分析系统浅析)

    5.2.2 随机森林回归

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  • from sklearn.ensemble import randomforestregressor
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  • rfr = randomforestregressor(n_estimators=100,random_state=42)
  • rfr.fit(x_train,y_train)
  • y_head_rfr = rfr.predict(x_test)
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  • print('real value of y_test[1]: '+str(y_test[1]) + ' -> predict value: ' + str(rfr.predict(x_test.iloc[[1],:])))
  • print('real value of y_test[2]: '+str(y_test[2]) + ' -> predict value: ' + str(rfr.predict(x_test.iloc[[2],:])))
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  • from sklearn.metrics import r2_score
  • print('r_square score: ',r2_score(y_test,y_head_rfr))
  • y_head_rfr_train = rfr.predict(x_train)
  • print('r_square score(train data):',r2_score(y_train,y_head_rfr_train))
  • python成绩判断系统(Python根据成绩分析系统浅析)

    5.2.3 决策树回归

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  • from sklearn.tree import decisiontreeregressor
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  • dt = decisiontreeregressor(random_state=42)
  • dt.fit(x_train,y_train)
  • y_head_dt = dt.predict(x_test)
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  • print('real value of y_test[1]: '+str(y_test[1]) + ' -> predict value: ' + str(dt.predict(x_test.iloc[[1],:])))
  • print('real value of y_test[2]: '+str(y_test[2]) + ' -> predict value: ' + str(dt.predict(x_test.iloc[[2],:])))
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  • from sklearn.metrics import r2_score
  • print('r_square score: ',r2_score(y_test,y_head_dt))
  • y_head_dt_train = dt.predict(x_train)
  • print('r_square score(train data):',r2_score(y_train,y_head_dt_train))
  • python成绩判断系统(Python根据成绩分析系统浅析)

    5.2.4 三种回归方法比较

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  • y = np.array([r2_score(y_test,y_head_lr),r2_score(y_test,y_head_rfr),r2_score(y_test,y_head_dt)])
  • x = np.arange(3)
  • plt.bar(x,y)
  • plt.title('comparion of regression algorithms')
  • plt.xlabel('regression')
  • plt.ylabel('r2_score')
  • plt.xticks(x,("linearregression","randomforestreg.","decisiontreereg."))
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论 : 回归算法中,线性回归的性能更优

    5.2.5 三种回归方法与实际值的比较

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  • ​red = plt.scatter(np.arange(0,80,5),y_head_lr[0:80:5],color='red')
  • blue = plt.scatter(np.arange(0,80,5),y_head_rfr[0:80:5],color='blue')
  • green = plt.scatter(np.arange(0,80,5),y_head_dt[0:80:5],color='green')
  • black = plt.scatter(np.arange(0,80,5),y_test[0:80:5],color='black')
  • plt.title('comparison of regression algorithms')
  • plt.xlabel('index of candidate')
  • plt.ylabel('chance of admit')
  • plt.legend([red,blue,green,black],['lr','rfr','dt','real'])
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:在数据集中有70%的候选人有可能读硕士,从上图来看还有些点没有很好的得到预测

    5.3 分类算法

    5.3.1 准备数据

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  • df = pd.read_csv('d:\\machine-learning\\score\\admission_predict.csv',sep=',')
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  • serialno = df['serial no.'].values
  • df.drop(['serial no.'],axis=1,inplace=true)
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  • df = df.rename(columns={'chance of admit ':'chance of admit'})
  • y = df['chance of admit'].values
  • x = df.drop(['chance of admit'],axis=1)
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  • 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=42)
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  • from sklearn.preprocessing import minmaxscaler
  • scalex = minmaxscaler(feature_range=[0,1])
  • x_train[x_train.columns] = scalex.fit_transform(x_train[x_train.columns])
  • x_test[x_test.columns] = scalex.fit_transform(x_test[x_test.columns])
  •  
  • # 如果chance >0.8, chance of admit 就是1,否则就是0
  • y_train_01 = [1 if each > 0.8 else 0 for each in y_train]
  • y_test_01 = [1 if each > 0.8 else 0 for each in y_test]
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  • y_train_01 = np.array(y_train_01)
  • y_test_01 = np.array(y_test_01)
  • 5.3.2 逻辑回归

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  • from sklearn.linear_model import logisticregression
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  • lrc = logisticregression()
  • lrc.fit(x_train,y_train_01)
  • print('score: ',lrc.score(x_test,y_test_01))
  • print('real value of y_test_01[1]: '+str(y_test_01[1]) + ' -> predict value: ' + str(lrc.predict(x_test.iloc[[1],:])))
  • print('real value of y_test_01[2]: '+str(y_test_01[2]) + ' -> predict value: ' + str(lrc.predict(x_test.iloc[[2],:])))
  •  
  • from sklearn.metrics import confusion_matrix
  • cm_lrc = confusion_matrix(y_test_01,lrc.predict(x_test))
  •  
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_lrc,annot=true,linewidths=0.5,linecolor='red',fmt='.0f',ax=ax)
  • plt.title('test for test dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  •  
  • from sklearn.metrics import recall_score,precision_score,f1_score
  • print('precision_score is : ',precision_score(y_test_01,lrc.predict(x_test)))
  • print('recall_score is : ',recall_score(y_test_01,lrc.predict(x_test)))
  • print('f1_score is : ',f1_score(y_test_01,lrc.predict(x_test)))
  •  
  • # test for train dataset:
  •  
  • cm_lrc_train = confusion_matrix(y_train_01,lrc.predict(x_train))
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_lrc_train,annot=true,linewidths=0.5,linecolor='blue',fmt='.0f',ax=ax)
  • plt.title('test for train dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.通过混淆矩阵,逻辑回归算法在训练集样本上,有23个分错的样本,有72人想进一步读硕士

    2.在测试集上有7个分错的样本 

    5.3.3 支持向量机(svm)

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  • from sklearn.svm import svc
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  • svm = svc(random_state=1,kernel='rbf')
  • svm.fit(x_train,y_train_01)
  • print('score: ',svm.score(x_test,y_test_01))
  • print('real value of y_test_01[1]: '+str(y_test_01[1]) + ' -> predict value: ' + str(svm.predict(x_test.iloc[[1],:])))
  • print('real value of y_test_01[2]: '+str(y_test_01[2]) + ' -> predict value: ' + str(svm.predict(x_test.iloc[[2],:])))
  •  
  • from sklearn.metrics import confusion_matrix
  • cm_svm = confusion_matrix(y_test_01,svm.predict(x_test))
  •  
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_svm,annot=true,linewidths=0.5,linecolor='red',fmt='.0f',ax=ax)
  • plt.title('test for test dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  •  
  • from sklearn.metrics import recall_score,precision_score,f1_score
  • print('precision_score is : ',precision_score(y_test_01,svm.predict(x_test)))
  • print('recall_score is : ',recall_score(y_test_01,svm.predict(x_test)))
  • print('f1_score is : ',f1_score(y_test_01,svm.predict(x_test)))
  •  
  • # test for train dataset:
  •  
  • cm_svm_train = confusion_matrix(y_train_01,svm.predict(x_train))
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_svm_train,annot=true,linewidths=0.5,linecolor='blue',fmt='.0f',ax=ax)
  • plt.title('test for train dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.通过混淆矩阵,svm算法在训练集样本上,有22个分错的样本,有70人想进一步读硕士

    2.在测试集上有8个分错的样本

    5.3.4 朴素贝叶斯

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  • from sklearn.naive_bayes import gaussiannb
  •  
  • nb = gaussiannb()
  • nb.fit(x_train,y_train_01)
  • print('score: ',nb.score(x_test,y_test_01))
  • print('real value of y_test_01[1]: '+str(y_test_01[1]) + ' -> predict value: ' + str(nb.predict(x_test.iloc[[1],:])))
  • print('real value of y_test_01[2]: '+str(y_test_01[2]) + ' -> predict value: ' + str(nb.predict(x_test.iloc[[2],:])))
  •  
  • from sklearn.metrics import confusion_matrix
  • cm_nb = confusion_matrix(y_test_01,nb.predict(x_test))
  •  
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_nb,annot=true,linewidths=0.5,linecolor='red',fmt='.0f',ax=ax)
  • plt.title('test for test dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  •  
  • from sklearn.metrics import recall_score,precision_score,f1_score
  • print('precision_score is : ',precision_score(y_test_01,nb.predict(x_test)))
  • print('recall_score is : ',recall_score(y_test_01,nb.predict(x_test)))
  • print('f1_score is : ',f1_score(y_test_01,nb.predict(x_test)))
  •  
  • # test for train dataset:
  •  
  • cm_nb_train = confusion_matrix(y_train_01,nb.predict(x_train))
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_nb_train,annot=true,linewidths=0.5,linecolor='blue',fmt='.0f',ax=ax)
  • plt.title('test for train dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.通过混淆矩阵,朴素贝叶斯算法在训练集样本上,有20个分错的样本,有78人想进一步读硕士

    2.在测试集上有7个分错的样本

    5.3.5 随机森林分类器

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  • from sklearn.ensemble import randomforestclassifier
  •  
  • rfc = randomforestclassifier(n_estimators=100,random_state=1)
  • rfc.fit(x_train,y_train_01)
  • print('score: ',rfc.score(x_test,y_test_01))
  • print('real value of y_test_01[1]: '+str(y_test_01[1]) + ' -> predict value: ' + str(rfc.predict(x_test.iloc[[1],:])))
  • print('real value of y_test_01[2]: '+str(y_test_01[2]) + ' -> predict value: ' + str(rfc.predict(x_test.iloc[[2],:])))
  •  
  • from sklearn.metrics import confusion_matrix
  • cm_rfc = confusion_matrix(y_test_01,rfc.predict(x_test))
  •  
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_rfc,annot=true,linewidths=0.5,linecolor='red',fmt='.0f',ax=ax)
  • plt.title('test for test dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  •  
  • from sklearn.metrics import recall_score,precision_score,f1_score
  • print('precision_score is : ',precision_score(y_test_01,rfc.predict(x_test)))
  • print('recall_score is : ',recall_score(y_test_01,rfc.predict(x_test)))
  • print('f1_score is : ',f1_score(y_test_01,rfc.predict(x_test)))
  •  
  • # test for train dataset:
  •  
  • cm_rfc_train = confusion_matrix(y_train_01,rfc.predict(x_train))
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_rfc_train,annot=true,linewidths=0.5,linecolor='blue',fmt='.0f',ax=ax)
  • plt.title('test for train dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.通过混淆矩阵,随机森林算法在训练集样本上,有0个分错的样本,有88人想进一步读硕士

    2.在测试集上有5个分错的样本

    5.3.6 决策树分类器

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  • from sklearn.tree import decisiontreeclassifier
  •  
  • dtc = decisiontreeclassifier(criterion='entropy',max_depth=3)
  • dtc.fit(x_train,y_train_01)
  • print('score: ',dtc.score(x_test,y_test_01))
  • print('real value of y_test_01[1]: '+str(y_test_01[1]) + ' -> predict value: ' + str(dtc.predict(x_test.iloc[[1],:])))
  • print('real value of y_test_01[2]: '+str(y_test_01[2]) + ' -> predict value: ' + str(dtc.predict(x_test.iloc[[2],:])))
  •  
  • from sklearn.metrics import confusion_matrix
  • cm_dtc = confusion_matrix(y_test_01,dtc.predict(x_test))
  •  
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_dtc,annot=true,linewidths=0.5,linecolor='red',fmt='.0f',ax=ax)
  • plt.title('test for test dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  •  
  • from sklearn.metrics import recall_score,precision_score,f1_score
  • print('precision_score is : ',precision_score(y_test_01,dtc.predict(x_test)))
  • print('recall_score is : ',recall_score(y_test_01,dtc.predict(x_test)))
  • print('f1_score is : ',f1_score(y_test_01,dtc.predict(x_test)))
  •  
  • # test for train dataset:
  •  
  • cm_dtc_train = confusion_matrix(y_train_01,dtc.predict(x_train))
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_dtc_train,annot=true,linewidths=0.5,linecolor='blue',fmt='.0f',ax=ax)
  • plt.title('test for train dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.通过混淆矩阵,决策树算法在训练集样本上,有20个分错的样本,有78人想进一步读硕士

    2.在测试集上有7个分错的样本

    5.3.7 k临近分类器

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  • from sklearn.neighbors import kneighborsclassifier
  •  
  • scores = []
  • for each in range(1,50):
  •  knn_n = kneighborsclassifier(n_neighbors = each)
  •  knn_n.fit(x_train,y_train_01)
  •  scores.append(knn_n.score(x_test,y_test_01))
  •  
  • plt.plot(range(1,50),scores)
  • plt.xlabel('k')
  • plt.ylabel('accuracy')
  • plt.show()
  •  
  •  
  • knn = kneighborsclassifier(n_neighbors=7)
  • knn.fit(x_train,y_train_01)
  • print('score 7 : ',knn.score(x_test,y_test_01))
  • print('real value of y_test_01[1]: '+str(y_test_01[1]) + ' -> predict value: ' + str(knn.predict(x_test.iloc[[1],:])))
  • print('real value of y_test_01[2]: '+str(y_test_01[2]) + ' -> predict value: ' + str(knn.predict(x_test.iloc[[2],:])))
  •  
  • from sklearn.metrics import confusion_matrix
  • cm_knn = confusion_matrix(y_test_01,knn.predict(x_test))
  •  
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_knn,annot=true,linewidths=0.5,linecolor='red',fmt='.0f',ax=ax)
  • plt.title('test for test dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  •  
  • from sklearn.metrics import recall_score,precision_score,f1_score
  • print('precision_score is : ',precision_score(y_test_01,knn.predict(x_test)))
  • print('recall_score is : ',recall_score(y_test_01,knn.predict(x_test)))
  • print('f1_score is : ',f1_score(y_test_01,knn.predict(x_test)))
  •  
  • # test for train dataset:
  •  
  • cm_knn_train = confusion_matrix(y_train_01,knn.predict(x_train))
  • f,ax = plt.subplots(figsize=(5,5))
  • sns.heatmap(cm_knn_train,annot=true,linewidths=0.5,linecolor='blue',fmt='.0f',ax=ax)
  • plt.title('test for train dataset')
  • plt.xlabel('predicted y values')
  • plt.ylabel('real y value')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    python成绩判断系统(Python根据成绩分析系统浅析)

    结论:1.通过混淆矩阵,k临近算法在训练集样本上,有22个分错的样本,有71人想进一步读硕士

    2.在测试集上有7个分错的样本

    5.3.8 分类器比较

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  • y = np.array([lrc.score(x_test,y_test_01),svm.score(x_test,y_test_01),nb.score(x_test,y_test_01),
  •     dtc.score(x_test,y_test_01),rfc.score(x_test,y_test_01),knn.score(x_test,y_test_01)])
  • x = np.arange(6)
  • plt.bar(x,y)
  • plt.title('comparison of classification algorithms')
  • plt.xlabel('classification')
  • plt.ylabel('score')
  • plt.xticks(x,("logisticreg.","svm","gnb","dec.tree","ran.forest","knn"))
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    结论:随机森林和朴素贝叶斯二者的预测值都比较高

    5.4 聚类算法

    5.4.1 准备数据

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  • df = pd.read_csv('d:\\machine-learning\\score\\admission_predict.csv',sep=',')
  • df = df.rename(columns={'chance of admit ':'chance of admit'})
  • serialno = df['serial no.']
  • df.drop(['serial no.'],axis=1,inplace=true)
  • df = (df - np.min(df)) / (np.max(df)-np.min(df))
  • y = df['chance of admit']
  • x = df.drop(['chance of admit'],axis=1)
  • 5.4.2 降维

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  • from sklearn.decomposition import pca
  •  
  • pca = pca(n_components=1,whiten=true)
  • pca.fit(x)
  • x_pca = pca.transform(x)
  • x_pca = x_pca.reshape(400)
  • dictionary = {'x':x_pca,'y':y}
  • data = pd.dataframe(dictionary)
  • print('pca data:',data.head())
  •  
  • print()
  •  
  • print('orin data:',df.head())
  • python成绩判断系统(Python根据成绩分析系统浅析)

    5.4.3 k均值聚类

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  • from sklearn.cluster import kmeans
  •  
  • wcss = []
  • for k in range(1,15):
  •  kmeans = kmeans(n_clusters=k)
  •  kmeans.fit(x)
  •  wcss.append(kmeans.inertia_)
  • plt.plot(range(1,15),wcss)
  • plt.xlabel('kmeans')
  • plt.ylabel('wcss')
  • plt.show()
  •  
  • df["serial no."] = serialno
  • kmeans = kmeans(n_clusters=3)
  • clusters_knn = kmeans.fit_predict(x)
  • df['label_kmeans'] = clusters_knn
  •  
  •  
  • plt.scatter(df[df.label_kmeans == 0 ]["serial no."],df[df.label_kmeans == 0]['chance of admit'],color = "red")
  • plt.scatter(df[df.label_kmeans == 1 ]["serial no."],df[df.label_kmeans == 1]['chance of admit'],color = "blue")
  • plt.scatter(df[df.label_kmeans == 2 ]["serial no."],df[df.label_kmeans == 2]['chance of admit'],color = "green")
  • plt.title("k-means clustering")
  • plt.xlabel("candidates")
  • plt.ylabel("chance of admit")
  • plt.show()
  •  
  • plt.scatter(data.x[df.label_kmeans == 0 ],data[df.label_kmeans == 0].y,color = "red")
  • plt.scatter(data.x[df.label_kmeans == 1 ],data[df.label_kmeans == 1].y,color = "blue")
  • plt.scatter(data.x[df.label_kmeans == 2 ],data[df.label_kmeans == 2].y,color = "green")
  • plt.title("k-means clustering")
  • plt.xlabel("x")
  • plt.ylabel("chance of admit")
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    python成绩判断系统(Python根据成绩分析系统浅析)

    结论:数据集分成三个类别,一部分学生是决定继续读硕士,一部分放弃,还有一部分学生的比较犹豫,但是深造的可能性较大

    5.4.4 层次聚类

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  • from scipy.cluster.hierarchy import linkage,dendrogram
  •  
  • merg = linkage(x,method='ward')
  • dendrogram(merg,leaf_rotation=90)
  • plt.xlabel('data points')
  • plt.ylabel('euclidean distance')
  • plt.show()
  •  
  • from sklearn.cluster import agglomerativeclustering
  •  
  • hiyerartical_cluster = agglomerativeclustering(n_clusters=3,affinity='euclidean',linkage='ward')
  • clusters_hiyerartical = hiyerartical_cluster.fit_predict(x)
  • df['label_hiyerartical'] = clusters_hiyerartical
  •  
  • plt.scatter(df[df.label_hiyerartical == 0 ]["serial no."],df[df.label_hiyerartical == 0]['chance of admit'],color = "red")
  • plt.scatter(df[df.label_hiyerartical == 1 ]["serial no."],df[df.label_hiyerartical == 1]['chance of admit'],color = "blue")
  • plt.scatter(df[df.label_hiyerartical == 2 ]["serial no."],df[df.label_hiyerartical == 2]['chance of admit'],color = "green")
  • plt.title('hierarchical clustering')
  • plt.xlabel('candidates')
  • plt.ylabel('chance of admit')
  • plt.show()
  •  
  • plt.scatter(data[df.label_hiyerartical == 0].x,data.y[df.label_hiyerartical==0],color='red')
  • plt.scatter(data[df.label_hiyerartical == 1].x,data.y[df.label_hiyerartical==1],color='blue')
  • plt.scatter(data[df.label_hiyerartical == 2].x,data.y[df.label_hiyerartical==2],color='green')
  • plt.title('hierarchical clustering')
  • plt.xlabel('x')
  • plt.ylabel('chance of admit')
  • plt.show()
  • python成绩判断系统(Python根据成绩分析系统浅析)

    python成绩判断系统(Python根据成绩分析系统浅析)

    结论:从层次聚类的结果中,可以看出和k均值聚类的结果一致,只不过确定了聚类k的取值3

    结论:通过本词入门数据集的训练,可以掌握

    1.一些特征的展示的方法

    2.如何调用sklearn 的api

    3.如何取比较不同模型之间的好坏

    代码+数据集:https://github.com/mounment/python-data-analyze/tree/master/kaggle/score

    原文链接:https://www.cnblogs.com/luhuajun/p/10361463.html

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