[英]Sentiment analysis/linear regression (Django)
I need a suggestion on how to do analyze this type of data. 我需要一个关于如何分析这类数据的建议。 I want to perform a sentiment analysis or linear regression on it as a machine learning tool.
我想对它进行情感分析或线性回归作为机器学习工具。 The predictor is score.
预测因子是得分。
color type make new score
red truck ford y 2
black sedan chevy n 4
silver sedan nissan y 5
silver truck nissan n 2
black coupe toyota y 1
blue van honda y 1
red truck toyota n 4
red coupe ford n 2
black sedan ford y 1
blue truck toyota y 4
white coupe chevy y 3
white van toyota n 5
red van ford y 2
silver truck nissan n 3
black sedan honda n 1
silver truck chevy y 4
red truck chevy y 5
white coupe honda n 5
blue sedan chevy n 2
blue van nissan y 3
I can run a LinearRegression classifier in WEKA which yields: 我可以在WEKA中运行LinearRegression分类器,它产生:
score = 1.6 ( color=red,silver,white) + 1.8 (make=honda,nissan,toyota,chevy) + 0.55
However, I would like to implement this in Django for a web app. 但是,我想在Django中实现这个Web应用程序。 Is there another way to process this data and yield a linear regression equation not using WEKA.
是否有另一种方法来处理这些数据并产生不使用WEKA的线性回归方程。 Any other suggestions on how to analyze it other than linear regression?
关于如何分析线性回归以外的任何其他建议? I've already implemented a decision tree.
我已经实现了一个决策树。
You can use scikit-learn as your machine learning library, and particularly its linear regression capability . 您可以使用scikit-learn作为您的机器学习库,特别是其线性回归功能 。 This example might also be useful.
这个例子也许有用。
Also, you can always bind the Weka java API to your application, or alternatively implement linear regression on your own, it is fairly easy algorithm to implement given a matrix algebra library. 此外,您始终可以将Weka java API绑定到您的应用程序,或者您自己实现线性回归,在给定矩阵代数库的情况下实现它是相当容易的算法。
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