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数据挖掘：银行客户认购产品预测。

数据数据来源阿里天池学习赛：零基础入门金融风控-贷款违约预测代码import pandas as pdimport numpy as npfrom sklearn.preprocessing import LabelEncoderfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import roc_auc_score,accuracy_scoreimport joblibfrom catboo

第一次用catboost训练模型时，出现报错，具体如下：CatBoostError: Invalid type for cat_feature[non-default value idx=0,feature_idx=4]=3.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.报错信息很好理解，训练器要求我们指定的类别变量的值必须为整型或

项目说明数据集来自阿里天池：会员回购预测目的是为了判断顾客是否会响应活动从而回购，也就是一个是否响应活动的二分类模型数据说明train&testtarget:“0”代表不回购，“1”代表回购transactions代码import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.model_selection import trai

Apriori 代码import pandas as pd#自定义连接函数，用于实现L_{k-1}到C_k的连接def connect_string(x, ms): """ x:频繁项集列表 ms: 连接符，这里用 ‘---’ return: 返回长度+1的频繁项集，即L_{k-1}到C_k的连接 """ x = list(map(lambda i:sorted(i....

数据来源：Baby Goods Info Data读取数据import numpy as npimport pandas as pdfrom pyecharts.charts import Pie,Bar,Pagefrom pyecharts import options as optsfilename = r'train.csv' #联结年龄和性别后的表filename1 = r'trade_history.csv'data = pd.read_csv(filename)data1 =

项目说明：数据来源阿里天池的一个挖掘比赛：预测二手车交易价格import pandas as pdimport matplotlib.pyplot as pltimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import mean_absolute_err...

数据来源：BlankerL数据截止2020-04-10下午4点json数据转 csvimport jsonimport timeimport csvfile = open('DXYArea-TimeSeries.json','r',encoding='utf-8')infos = json.load(file)with open('data.csv','a',newline='...

时间序列分析移动平均import pandas as pdimport numpy as npdata = pd.DataFrame([17,21,19,23,18,16,20,18,22,20,15,22],columns=['sale'])data["rolling_mean"] = data.rolling(3).mean().shift(1)#选前3个时间单位的平均值，预测值...

一元线性回归import pandas as pdimport numpy as npimport statsmodels.api as sm# import statsmodels.formula.api as smf方法二x = np.array([2,6,8,8,12,16,20,20,22,26])y = np.array([58,105,88,118,117,137,15...

方差分析单因素方差分析（多个总体均值相等的假设检验）import scipy.stats as statsa = [58,64,55,66,67]b = [58,69,71,64,68]c = [48,57,59,47,49]stats.f_oneway(a,b,c)F_onewayResult(statistic=9.176470588235295, pvalue=0.003...

总体方差的置信区间from scipy.stats import chi2def CI(n,sigma2,alpha): """ n:样本量 sigma2:样本方差 alpha:置信水平 """ a = (1-alpha)/2 b = (n-1)*sigma2 lower = b/chi2.ppf(a,df=n-1) ...

独立样本T检验（两样本均值估计两总体均值大小关系）import pandas as pdimport numpy as npfrom scipy import stats# 传数据data = pd.read_excel(r"C:\Users\liuhao\Desktop\python_work\Python数据分析与挖掘实战\chapter8\test\data\data.xls")...