It can't be used. . Data preprocessing Before we confirm

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Taking our company's project as an example, my functional evaluation is a non-functional evaluation. Determine the scoring guide. Product managers need to develop a set of standard scoring guidelines that can facilitate the interpretation of model scoring rather than scoring too subjectively. Example: Completely meet the requirements without changing a word. Adopt directly. Not completely meet the requirements with minor flaws but acceptable. Adopt after minor changes. Not completely meet the requirements with major flaws. Although they can be changed, it is more troublesome to change.If it won't change, just throw it away.



If it's not satisfactory at all, it's wrong and off-topic.preprocess iran phone number format the existing documents. Because the company's previous documents are reference manuals I wrote for the team and partners, there must be some oral problems and inconsistent formats. In order to make the big model better understand the content in the corporate documents, I performed the following operations. Data normalization mzt Scaling values ​​Scale the data to a specific range or ratio, such as to between to eliminate the influence of different numerical ranges and dimensions.



Normalization Convert the data to a format with a uniform ratio. Common methods include minimum-maximum normalization, Z-score normalization, etc. Encoding categorical variables Convert categorical variables to a format that the model can handle, such as using one-hot encoding-Ht or label encoding. Feature Engineering Create new features or modify existing features to improve the performance of the model, such as through polynomial feature expansion or interaction terms. Dimensionality Reduction Use techniques such as principal component analysis to reduce the number of features while retaining as much variability as possible in the original data. Explanation Explain the unique jargon in the document to avoid bias in the understanding of the large model.