官术网_书友最值得收藏!

How to do it...

In the following steps, we utilize scikit-learn's StandardScaler method to standardize our data:

  1. Start by importing the required libraries and gathering a dataset, X:
import pandas as pd

data = pd.read_csv("file_pe_headers.csv", sep=",")
X = data.drop(["Name", "Malware"], axis=1).to_numpy()

Dataset X looks as follows:

  1. Next, standardize X using a StandardScaler instance:
from sklearn.preprocessing import StandardScaler

X_standardized = StandardScaler().fit_transform(X)

The standardized dataset looks like the following:

主站蜘蛛池模板: 凭祥市| 柳江县| 安仁县| 洛阳市| 武陟县| 龙江县| 新河县| 大新县| 荃湾区| 天等县| 乌鲁木齐市| 东乡族自治县| 罗平县| 绥阳县| 姜堰市| 保亭| 武义县| 日土县| 阿巴嘎旗| 蓬溪县| 柳林县| 息烽县| 柏乡县| 获嘉县| 龙陵县| 炉霍县| 三原县| 乐东| 托克逊县| 桐城市| 靖宇县| 武鸣县| 上林县| 荆州市| 时尚| 和顺县| 额敏县| 兰坪| 宣武区| 水富县| 广元市|