基于python的线性回归分析项目源码+文档说明课设（95分以上）.zip

# 基于python的回归分析 利用python进行回归分析，首先要导入python的扩展程序库，包括numpy程序库、matplotlib.pyplot和tensorflow，因为下载的tensorflow版本较高，所以应当导入低版本的，同时要使用特定指令使得低版本的指令可以使用。 在开始进行回归分析时，首先利用python生成按照公式 $$y=0.1x+0.3$$ 进行随机偏移的100个点作为原始数据进行回归分析，生成的数据点如图所示。 ![图片](data:image/png;base64,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