注意：数据由实习单位老师提供（需要自行搜索下载），页面美化为下载模板。

项目介绍：前端页面输入影响成绩的属性，预测出成绩，并作可视化展示——属性对成绩的影响。使用python pyspark 进行数据预处理、探索性数据分析可视化、调用模型、对比模、型调、优评估等。

成果展示：

1.页面功能展示

2.输入影响成绩因素值——预测成绩

 3.可视化部分

 

 4.pyspark代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from Cython import inline
from matplotlib.font_manager import FontProperties
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
from sklearn import preprocessing, metrics, svm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing, metrics, svm
from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error
import scipy
import pickle
import seaborn as sns
from sympy.physics.quantum.circuitplot import matplotlib
sns.set(font_scale=1.5)
import warnings
warnings.filterwarnings("ignore")# 初始化数据
plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文字体设置-黑体
plt.rcParams['axes.unicode_minus'] = False   # 解决保存图像是负号'-'显示为方块的问题
sns.set(font='SimHei')                       # 解决Seaborn中文显示问题
plt.rcParams['figure.dpi'] = 100
plt.rcParams['figure.figsize'] = (5,3)
plt.style.use('seaborn-darkgrid')student = pd.read_csv('../data/student-mat.csv')
data = pd.read_csv('../data/student-mat.csv')
df=pd.read_csv('../data/student-mat.csv')
#print(df.columns)
#student["G3"].describe()
#print(student.isna().sum())   # 统计数据集各列缺失值个数
#student.info()  #来查看一下变量的数据类型
most_correlated1 = student.corr().abs()['G3'].sort_values(ascending=False)
most_correlated1 = most_correlated1[:15]
print(most_correlated1)student = pd.get_dummies(student)
#print(student.columns)
# 选取相关性最强的8个
most_correlated = student.corr().abs()['G3'].sort_values(ascending=False)
most_correlated = most_correlated[:15]
print(most_correlated)y=data["G3"]
# 选取G3属性值
labels = data["G3"]
print(most_correlated.index)
# 删除school，G1和G2属性
data=data[['G3','failures', 'Medu', 'age','Fedu','goout','traveltime','romantic','higher']]
feature=data.columns
data = data.drop(labels=["G3"],axis="columns")
print(data)
# 对离散变量进行独热编码
data = pd.get_dummies(data)
print(data.columns)
#y = pd.get_dummies(y )X_train,X_test,y_train,y_test=train_test_split(data,y,test_size=0.15,random_state=42)model5=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
model5=model5.fit(X_train,y_train)
y_pred5=model5.predict(X_test)print('线性回归可解释方差值：{}'.format(round(metrics.explained_variance_score(y_test, y_pred5), 2)))
print('线性回归平均绝对误差：{}'.format(round(metrics.mean_absolute_error(y_test, y_pred5), 2)))
print('线性回归均方误差：{}'.format(round(np.sqrt(np.mean((y_pred5- y_test) ** 2)))))
print('线性回归 R方值：{}'.format(round(metrics.r2_score(y_test, y_pred5), 2)))LR_model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
# 拟合
model=LR_model.fit(X_train, y_train)
filename = '../modelR/LR_Model'       #保存为当前文件夹下model文件夹里面 命名XGB_Model
# 序列化 对象以二进制方式保存到硬盘 write， b=二进制
pickle.dump(model, open(filename, 'wb'))# 加载模型
with open('../modelR/LR_Model', 'rb') as model:# 反序列化对象模型   read b=二进制loaded_model = pickle.load(model)# print(X_test.head(1))# 使用加载的模型进行预测predictions = loaded_model.predict(X_test.head(5))print(predictions)