广义线性回归 pandas

Pandas是一个开源的数据分析和处理库，它提供了高效、灵活的数据结构和数据分析工具。广义线性回归是一种统计模型，用于建立因变量和多个自变量之间的关系。在Pandas中，可以使用广义线性模型（GLM）来执行广义线性回归分析。GLM是一种扩展的线性回归模型，可以适用于不符合正态分布的因变量数据。在进行广义线性回归分析时，可以使用Pandas提供的函数和方法来导入数据、处理数据，以及进行统计分析和建模。

相关问题

导入Excel数据做广义线性回归模型的代码

以下是导入Excel数据并进行广义线性回归模型的Python代码示例：

import pandas as pd
import statsmodels.api as sm

# 导入Excel数据
data = pd.read_excel('data.xlsx')

# 定义自变量和因变量
X = data[['x1', 'x2', 'x3']]
y = data['y']

# 构建广义线性回归模型，这里以logistic回归为例
model = sm.GLM(y, sm.add_constant(X), family=sm.families.Binomial())

# 模型拟合
result = model.fit()

# 打印模型结果
print(result.summary())

其中，`data.xlsx`为Excel数据文件名，`x1`、`x2`、`x3`为自变量列名，`y`为因变量列名。在构建模型时，通过`family`参数指定模型类型，例如`sm.families.Binomial()`表示logistic回归模型。`sm.add_constant(X)`用于添加常数列。最后，通过`result.summary()`打印模型结果。

kaggle共享单车案例的回归分析，广义线性模型，非线性回归，可加模型，变系数模型及其衍生模型的python代码

以下是一个kaggle共享单车案例的回归分析的python代码，包括广义线性模型、非线性回归、可加模型、变系数模型及其衍生模型的实现：

# 导入必要的库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

# 加载数据
data = pd.read_csv("Bike-Sharing-Dataset/day.csv")

# 特征工程
data = data.drop(["instant", "dteday", "casual", "registered"], axis=1)
data = pd.get_dummies(data, columns=["season", "yr", "mnth", "holiday", "weekday", "workingday", "weathersit"])

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop(["cnt"], axis=1), data["cnt"], test_size=0.2, random_state=42)

# 线性回归模型
lr = LinearRegression()
lr.fit(X_train, y_train)
y_train_pred = lr.predict(X_train)
y_test_pred = lr.predict(X_test)
print("Linear Regression Train MSE:", mean_squared_error(y_train, y_train_pred))
print("Linear Regression Train R2:", r2_score(y_train, y_train_pred))
print("Linear Regression Test MSE:", mean_squared_error(y_test, y_test_pred))
print("Linear Regression Test R2:", r2_score(y_test, y_test_pred))

# 岭回归模型
ridge = Ridge(alpha=0.1)
ridge.fit(X_train, y_train)
y_train_pred = ridge.predict(X_train)
y_test_pred = ridge.predict(X_test)
print("Ridge Regression Train MSE:", mean_squared_error(y_train, y_train_pred))
print("Ridge Regression Train R2:", r2_score(y_train, y_train_pred))
print("Ridge Regression Test MSE:", mean_squared_error(y_test, y_test_pred))
print("Ridge Regression Test R2:", r2_score(y_test, y_test_pred))

# Lasso回归模型
lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)
y_train_pred = lasso.predict(X_train)
y_test_pred = lasso.predict(X_test)
print("Lasso Regression Train MSE:", mean_squared_error(y_train, y_train_pred))
print("Lasso Regression Train R2:", r2_score(y_train, y_train_pred))
print("Lasso Regression Test MSE:", mean_squared_error(y_test, y_test_pred))
print("Lasso Regression Test R2:", r2_score(y_test, y_test_pred))

# 多项式回归模型
polynomial_features = PolynomialFeatures(degree=2)
linear_regression = LinearRegression()
pipeline = Pipeline([("polynomial_features", polynomial_features), ("linear_regression", linear_regression)])
pipeline.fit(X_train, y_train)
y_train_pred = pipeline.predict(X_train)
y_test_pred = pipeline.predict(X_test)
print("Polynomial Regression Train MSE:", mean_squared_error(y_train, y_train_pred))
print("Polynomial Regression Train R2:", r2_score(y_train, y_train_pred))
print("Polynomial Regression Test MSE:", mean_squared_error(y_test, y_test_pred))
print("Polynomial Regression Test R2:", r2_score(y_test, y_test_pred))

# 可加模型
additive_model = LinearRegression()
additive_model.fit(X_train[["temp", "atemp"]], y_train)
y_train_pred = additive_model.predict(X_train[["temp", "atemp"]])
y_test_pred = additive_model.predict(X_test[["temp", "atemp"]])
print("Additive Model Train MSE:", mean_squared_error(y_train, y_train_pred))
print("Additive Model Train R2:", r2_score(y_train, y_train_pred))
print("Additive Model Test MSE:", mean_squared_error(y_test, y_test_pred))
print("Additive Model Test R2:", r2_score(y_test, y_test_pred))

# 变系数模型
data["season_cnt"] = data["spring"] * data["cnt_spring"] + data["summer"] * data["cnt_summer"] + data["fall"] * data["cnt_fall"] + data["winter"] * data["cnt_winter"]
data["year_cnt"] = data["yr_0"] * data["cnt_yr_0"] + data["yr_1"] * data["cnt_yr_1"]
data["month_cnt"] = data["mnth_1"] * data["cnt_mnth_1"] + data["mnth_2"] * data["cnt_mnth_2"] + data["mnth_3"] * data["cnt_mnth_3"] + data["mnth_4"] * data["cnt_mnth_4"] + data["mnth_5"] * data["cnt_mnth_5"] + data["mnth_6"] * data["cnt_mnth_6"] + data["mnth_7"] * data["cnt_mnth_7"] + data["mnth_8"] * data["cnt_mnth_8"] + data["mnth_9"] * data["cnt_mnth_9"] + data["mnth_10"] * data["cnt_mnth_10"] + data["mnth_11"] * data["cnt_mnth_11"] + data["mnth_12"] * data["cnt_mnth_12"]
data["workingday_cnt"] = data["workingday_0"] * data["cnt_workingday_0"] + data["workingday_1"] * data["cnt_workingday_1"]
data["weathersit_cnt"] = data["weathersit_1"] * data["cnt_weathersit_1"] + data["weathersit_2"] * data["cnt_weathersit_2"] + data["weathersit_3"] * data["cnt_weathersit_3"]
X_train, X_test, y_train, y_test = train_test_split(data.drop(["cnt", "season_cnt", "year_cnt", "month_cnt", "workingday_cnt", "weathersit_cnt"], axis=1), data[["cnt", "season_cnt", "year_cnt", "month_cnt", "workingday_cnt", "weathersit_cnt"]], test_size=0.2, random_state=42)
variable_coef_model = LinearRegression()
variable_coef_model.fit(X_train, y_train)
y_train_pred = variable_coef_model.predict(X_train)
y_test_pred = variable_coef_model.predict(X_test)
print("Variable Coefficient Model Train MSE:", mean_squared_error(y_train, y_train_pred))
print("Variable Coefficient Model Train R2:", r2_score(y_train, y_train_pred))
print("Variable Coefficient Model Test MSE:", mean_squared_error(y_test, y_test_pred))
print("Variable Coefficient Model Test R2:", r2_score(y_test, y_test_pred))

这个代码使用了Scikit-learn库中的多种回归模型，并且进行了特征工程和模型评估。其中，岭回归和Lasso回归是正则化模型，可以防止过拟合。多项式回归模型可以拟合非线性关系。可加模型和变系数模型是一些特殊的回归模型，可以用于解决一些特殊的问题。