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用机器学习进行学生成绩预测的数据分析（入门向 附可用源码）

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用机器学习进行学生成绩预测的数据分析（入门向 附可用源码）

2024-07-12 14:15| 来源: 网络整理| 查看: 265

—数据集见评论区—

用机器学习进行学生成绩预测的数据分析（入门向 附可用源码） ---数据集见评论区--- 声明思路检查数据图像化处理分析相关性分析构建模型 代码实现可运行代码

声明

文章代码修改于kaggle博主DIPAMVASANI，本文旨在将精华的内容留住并加以分析，防止繁杂的信息扰乱该文的本质，因此删除了图形化处理模块并在核心代码处加了中文注释。

思路 检查数据

数据的检测是很重要的，之前没有好好地检查数据，导致很多有用的信息没有看到，因此要先检查数据集的数据，保证核心的信息不遗漏，例如：y值（待预测值）和x值（属性值）一定要分清。

图像化处理

这个部分代码由于最后的分析实际上不需要，因此删除。

分析 相关性分析

首先要注意非数值型变量进行one-hot编码。 然后直接去计算目标数据和各属性的相关性矩阵，并取topK个用作最后的建模。

构建模型

先用训练集的均值预测一下试试，如果后面的模型比均值还烂。。。那拜拜了您内。 最后分数据集分一分，注意随机种子，分好之后用各个模型跑出误差，挑选最优，即完成。

如果感觉这部分没有看懂，没有关系，后面我们会详细讲解。

代码实现

首先读入数据并用describe函数查看大致属性

# Some basic analysis student = pd.read_csv('./input/student-mat.csv') print(student.head()) print('Total number of students:',len(student)) print(student['G3'].describe())

然后对整个数据集进行one-hot编码

# Encoding categorical variables # Select only categorical variables category_df = student.select_dtypes(include=['object']) # 挑选非数值型变量 # One hot encode the variables dummy_df = pd.get_dummies(category_df) # get_dummies是实现one-hot编码的方法 # Put the grade back in the dataframe dummy_df['G3'] = student['G3'] # Find correlations with grade dummy_df.corr()['G3'].sort_values() # Applying one hot encoding to our data and finding correlation again # selecting the most correlated values and dropping the others labels = student['G3'] # G3那一列的数值 # drop the school and grade columns student = student.drop(['school', 'G1', 'G2'], axis='columns') # 删除三列 # One-Hot Encoding of Categorical Variables student = pd.get_dummies(student) # 对整个数据集进行one-hot编码

随后进行相关性分析

# Find correlations with the Grade 生成与G3相关度的排序列（降序排列） most_correlated = student.corr().abs()['G3'].sort_values(ascending=False) # student.corr()生成相关矩阵 .abs()取绝对值 ['G3']挑出G3列 sort默认升序 # Maintain the top 8 most correlation features with Grade most_correlated = most_correlated[:9] # 其实就是[0:9]有0无9 最相关的9个变量（G3是自己 除G3外8个） print(most_correlated) student = student.loc[:, most_correlated.index] # .loc 通过label选定一组行或列 第一个是行 逗号后面是列 选定最相关的九个变量 旗下每个学生的属性值 print(student.head())

然后运用最简单的均值预测作为我们的基线方法

# splitting the data into training and testing data (75% and 25%) # we mention the random state to achieve the same split everytime we run the code #train_test_split(训练数据，样本结果，测试集的样本占比，random_state=None(default)每次划分不同，为整数则相同) X_train, X_test, y_train, y_test = train_test_split(student, labels, test_size = 0.25, random_state=42) print(X_train.head()) # Calculate mae and rmse def evaluate_predictions(predictions, true): mae = np.mean(abs(predictions - true)) # 平均绝对误差(np.mean需要是数组) rmse = np.sqrt(np.mean((predictions - true) ** 2)) # 均方根误差(np.sqrt同样需要是数组) return mae, rmse # find the median median_pred = X_train['G3'].median() # 取中位数 # create a list with all values as median median_preds = [median_pred for _ in range(len(X_test))] # 生成长度为测试集的全值为median_pred的列表 # store the true G3 values for passing into the function true = X_test['G3'] # 保存真实的结果 # Display the naive baseline metrics mb_mae, mb_rmse = evaluate_predictions(median_preds, true) print('Median Baseline MAE: {:.4f}'.format(mb_mae)) print('Median Baseline RMSE: {:.4f}'.format(mb_rmse))

最后跑模型，生成结果（最后的部分是唯一生成图片的部分，用于生成模型的比较，需要掌握）

results = evaluate(X_train, X_test, y_train, y_test) print(results) plt.figure(figsize=(12, 8)) # Root mean squared error ax = plt.subplot(1, 2, 1) results.sort_values('mae', ascending = True).plot.bar(y = 'mae', color = 'b', ax = ax, fontsize=20) plt.title('Model Mean Absolute Error', fontsize=20) plt.ylabel('MAE', fontsize=20) # Median absolute percentage error ax = plt.subplot(1, 2, 2) results.sort_values('rmse', ascending = True).plot.bar(y = 'rmse', color = 'r', ax = ax, fontsize=20) plt.title('Model Root Mean Squared Error', fontsize=20) plt.ylabel('RMSE',fontsize=20) plt.show() 可运行代码 import pandas as pd from matplotlib import pyplot as plt import numpy as np from sklearn.linear_model import LinearRegression from sklearn.linear_model import ElasticNet from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import ExtraTreesRegressor from sklearn.ensemble import GradientBoostingRegressor from sklearn.svm import SVR # Splitting data into training/testing from sklearn.model_selection import train_test_split # Some basic analysis student = pd.read_csv('./input/student-mat.csv') print(student.head()) print('Total number of students:',len(student)) print(student['G3'].describe()) # Correlation print(student.corr()['G3'].sort_values()) # Encoding categorical variables # Select only categorical variables category_df = student.select_dtypes(include=['object']) # 挑选非数值型变量 # One hot encode the variables dummy_df = pd.get_dummies(category_df) # get_dummies是实现one-hot编码的方法 # Put the grade back in the dataframe dummy_df['G3'] = student['G3'] # Find correlations with grade dummy_df.corr()['G3'].sort_values() # Applying one hot encoding to our data and finding correlation again # selecting the most correlated values and dropping the others labels = student['G3'] # G3那一列的数值 # drop the school and grade columns student = student.drop(['school', 'G1', 'G2'], axis='columns') # 删除三列 # One-Hot Encoding of Categorical Variables student = pd.get_dummies(student) # 对整个数据集进行one-hot编码 # Find correlations with the Grade 生成与G3相关度的排序列（降序排列） most_correlated = student.corr().abs()['G3'].sort_values(ascending=False) # student.corr()生成相关矩阵 .abs()取绝对值 ['G3']挑出G3列 sort默认升序 # Maintain the top 8 most correlation features with Grade most_correlated = most_correlated[:9] # 其实就是[0:9]有0无9 最相关的9个变量（G3是自己 除G3外8个） print(most_correlated) student = student.loc[:, most_correlated.index] # .loc 通过label选定一组行或列 第一个是行 逗号后面是列 选定最相关的九个变量 旗下每个学生的属性值 print(student.head()) # splitting the data into training and testing data (75% and 25%) # we mention the random state to achieve the same split everytime we run the code #train_test_split(训练数据，样本结果，测试集的样本占比，random_state=None(default)每次划分不同，为整数则相同) X_train, X_test, y_train, y_test = train_test_split(student, labels, test_size = 0.25, random_state=42) print(X_train.head()) # Calculate mae and rmse def evaluate_predictions(predictions, true): mae = np.mean(abs(predictions - true)) # 平均绝对误差(np.mean需要是数组) rmse = np.sqrt(np.mean((predictions - true) ** 2)) # 均方根误差(np.sqrt同样需要是数组) return mae, rmse # find the median median_pred = X_train['G3'].median() # 取中位数 # create a list with all values as median median_preds = [median_pred for _ in range(len(X_test))] # 生成长度为测试集的全值为median_pred的列表 # store the true G3 values for passing into the function true = X_test['G3'] # 保存真实的结果 # Display the naive baseline metrics mb_mae, mb_rmse = evaluate_predictions(median_preds, true) print('Median Baseline MAE: {:.4f}'.format(mb_mae)) print('Median Baseline RMSE: {:.4f}'.format(mb_rmse)) # Evaluate several ml models by training on training set and testing on testing set def evaluate(X_train, X_test, y_train, y_test): # Names of models model_name_list = ['Linear Regression', 'ElasticNet Regression', 'Random Forest', 'Extra Trees', 'SVM', 'Gradient Boosted', 'Baseline'] X_train = X_train.drop('G3', axis='columns') X_test = X_test.drop('G3', axis='columns') # Instantiate the models model1 = LinearRegression() model2 = ElasticNet() # 结合岭回归和Lasso回归，避免过拟合 model3 = RandomForestRegressor() # 多个决策树 model4 = ExtraTreesRegressor() # 类随机森林 model5 = SVR() # SVM的反义词 model6 = GradientBoostingRegressor(n_estimators=50) # 梯度提升回归 # Dataframe for results results = pd.DataFrame(columns=['mae', 'rmse'], index=model_name_list) # columns列 index行 # Train and predict with each model for i, model in enumerate([model1, model2, model3, model4, model5, model6]): model.fit(X_train, y_train) predictions = model.predict(X_test) # Metrics mae = np.mean(abs(predictions - y_test)) rmse = np.sqrt(np.mean((predictions - y_test) ** 2)) # Insert results into the dataframe model_name = model_name_list[i] results.loc[model_name, :] = [mae, rmse] # Median Value Baseline Metrics baseline = np.median(y_train) baseline_mae = np.mean(abs(baseline - y_test)) baseline_rmse = np.sqrt(np.mean((baseline - y_test) ** 2)) results.loc['Baseline', :] = [baseline_mae, baseline_rmse] return results results = evaluate(X_train, X_test, y_train, y_test) print(results) plt.figure(figsize=(12, 8)) # Root mean squared error ax = plt.subplot(1, 2, 1) results.sort_values('mae', ascending = True).plot.bar(y = 'mae', color = 'b', ax = ax, fontsize=20) plt.title('Model Mean Absolute Error', fontsize=20) plt.ylabel('MAE', fontsize=20) # Median absolute percentage error ax = plt.subplot(1, 2, 2) results.sort_values('rmse', ascending = True).plot.bar(y = 'rmse', color = 'r', ax = ax, fontsize=20) plt.title('Model Root Mean Squared Error', fontsize=20) plt.ylabel('RMSE',fontsize=20) plt.show()


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