資源簡介
主要有如下部分：1.如何安裝Python 和相關機器學習的庫模塊； 2.如何用數據庫里面的數據; 3. 用不同的機器學習算法對數據庫里的數據進行分類預測并比較各種預測算法的準確性； 4. 選擇最優算法進行預測
代碼片段和文件信息
#?Load?libraries
import?pandas
#?from?pandas.tools.plotting?import?scatter_matrix
import?matplotlib.pyplot?as?plt
from?sklearn?import?model_selection
from?sklearn.metrics?import?classification_report
from?sklearn.metrics?import?confusion_matrix
from?sklearn.metrics?import?accuracy_score
from?sklearn.linear_model?import?LogisticRegression
from?sklearn.tree?import?DecisionTreeClassifier
from?sklearn.neighbors?import?KNeighborsClassifier
from?sklearn.discriminant_analysis?import?LinearDiscriminantAnalysis
from?sklearn.naive_bayes?import?GaussianNB
from?sklearn.svm?import?SVC
#?Load?dataset
url?=?“https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data“
names?=?[‘sepal-length‘?‘sepal-width‘?‘petal-length‘?‘petal-width‘?‘class‘]
dataset?=?pandas.read_csv（ 評論
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