数据分析模型之多元回归Python代码

多元回归

  # 导⼊模块
  from sklearn import model_selection
  # 导⼊数据
  Profit = pd.read_excel(r\'Predict to Profit.xlsx\')
  # 将数据集拆分为训练集和测试集
  train, test = model_selection.train_test_split(Profit, 
                                                 test_size = 0.2, 
                                                 random_state=1234
                                                )
  # 根据train数据集建模
  model = sm.formula.ols(\'Profit ~ RD_Spend+Administration+Marketing_Spend+C(State)\', data= train).fit()
  # print(\'模型的偏回归系数分别为：\n\', model.params)
  # 删除test数据集中的Profit变量，⽤剩下的⾃变量进⾏预测
  test_X = test.drop(labels = \'Profit\', axis = 1)

  pred = model.predict(exog = test_X)
  print(\'对⽐预测值和实际值的差异：\n\',pd.DataFrame({\'Prediction\':pred,\'Real\':test.Profit}))

由于地区自变量存在多重共线性，所以系统会自动删除一个，当然也可以自定义一个

  # ⽣成由State变量衍⽣的哑变量
  dummies = pd.get_dummies(Profit.State)
  # 将哑变量与原始数据集⽔平合并
  Profit_New = pd.concat([Profit,dummies], axis = 1)
  # 删除State变量和California变量(因为State变量已被分解为哑变量，New York变量需要作为参照组)
  Profit_New.drop(labels = [\'State\',\'New York\'], axis = 1, inplace = True)
  # 拆分数据集Profit_New
  train, test = model_selection.train_test_split(Profit_New, test_size = 0.2, random_state=1234)
  # 建模
  model2 = sm.formula.ols(\'Profit~RD_Spend+Administration+Marketing_Spend+Florida+California\',
  data = train).fit()
  print(\'模型的偏回归系数分别为:\n\', model2.params)