多个机器学习模型对在线购物意向数据集的分类
多个机器学习模型对 Online Shoppers Purchasing Intention Dataset Data Set数据集的分类
注：数据源网址https://archive.ics.uci.edu/ml/datasets/Online+Shoppers+Purchasing+Intention+Dataset
注：源码地址https://github.com/HY1506698498/-/tree/main/%E8%B4%AD%E7%89%A9%E6%84%8F%E5%90%91
1.KNN
import pandas as pd
df = pd.read_csv('./online_shoppers_intention.csv') df.head() df.shape df.describe() df.columns X = df[['Administrative', 'Administrative_Duration', 'Informational', 'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration', 'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay', 'OperatingSystems', 'Browser', 'Region', 'TrafficType', 'Weekend',]] y = df['Revenue'] from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import cross_val_score rfc = RandomForestClassifier(random_state=90) base_score = cross_val_score(rfc, X, y, cv=10).mean() base_score from sklearn.model_selection import GridSearchCV import numpy as np rfc = RandomForestClassifier(random_state=90, n_jobs=-1) param_gird = ({ 'n_estimators': np.arange(1, 201, 10) }) clf = GridSearchCV(rfc,param_grid=param_gird, cv=10) clf.fit(X, y) clf.best_score_, clf.best_params_ from sklearn.model_selection import GridSearchCV import numpy as np rfc = RandomForestClassifier(random_state=90, n_jobs=-1) param_gird = ({ 'n_estimators': np.arange(170, 211, 1) }) clf = GridSearchCV(rfc,param_grid=param_gird, cv=10) clf.fit(X, y) clf.best_score_, clf.best_params_ score1 = [] for i in range(1, 20, 1): rfc = RandomForestClassifier( n_estimators=195, n_jobs=-1, random_state=90, max_depth= i ) score = cross_val_score(rfc, X, y, cv = 10).mean() score1.append(score) print(max(score1)
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