def get_k_fold_data(k, i, X, y): assert k > 1 fold_size = X.shape[0] // k X_train, y_train = None, None for j in range(k): idx = slice(j * fold_size, (j + 1) * fold_size) X_part, y_part = X[idx, :], y[idx] if j == i: X_valid, y_valid = X_pa...
#saleprice correlation matrix k=10#numberofvariablesforheatmap cols=corrmat.nlargest(k,'SalePrice')['SalePrice'].index cm=np.corrcoef(train_df[cols].values.T)sns.set(font_scale=1.25)hm=sns.heatmap(cm,cbar=True,annot=True,square=True,fmt='.2f',annot_kws={'size':10},yticklabels=cols...
sns.barplot(x=data['Gender'].value_counts().index, y=data['Gender'].value_counts().values) plt.title('Genders other rate') plt.ylabel('Rates') plt.show() plt.figure(figsize=(7,7)) sns.barplot(x=data['Race/Ethnicity'].value_counts().index,y=data['Race/Ethnicity'].value_counts(...
比赛结束后学习排名靠前的队伍的方法，思考自己这次比赛中的不足和发现的问题，可能的话再花点时间将学到的新东西用实验进行确认，为下一次比赛做准备。ReferenceBeating Kaggle the Easy Way - Dong YingSolution for Prudential Life Insurance Assessment - NutastraySearch Results Relevance Winner’s Interview: 1st...
scatterplot(data=df_frequency, x="Customer_Age", y="Total_Trans_Ct", hue="Attrition_Flag", ax=ax[3]) plt.show() 基于plotly的实现: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 for col in ["Customer_Age","Total_Trans_Amt","Months_Inactive_12_mon","Credit_Limit"]: fig = ...
() # 将数据转换成列表嵌套字典类型数据 方便饼图数据渲染 data1 = [{'name':i.pos,'value':i.count} for i in pos_stars_count] data2 = [{'name':i.height_area,'value':i.count} for i in height_stars_count] return render_template('drawPie.html',**locals()) # NBA球员的年龄与得分...
Learn the most important language for data science. Intro to Machine Learning 3 hours to complete Learn the core ideas in machine learning, and build your first models. Pandas 4 hours to complete Solve short hands-on challenges to perfect your data manipulation skills. ...
Step1: Exploratory Data Analysis EDA,也就是对数据进行探索性的分析,一般就用到pandas和matplotlib就够了。EDA一般包括: 每个feature的意义,feature的类型,比较有用的代码如下 df.describe() df['Category'].unique() 1. 2. 看是否存在missing value
fig.set(alpha=0.2)# 设定图表颜色alpha参数data_train.Survived.value_counts().plot(kind='bar')# 柱状图plt.title(u"获救情况 (1为获救)")# 标题plt.ylabel(u"人数") plt.show() 1.2 PClass # PClassdefpclass_analysis(data_train): fig = plt.figure() ...
comp$Set[1:nrow(train)] <- "Train" comp$Set[(nrow(train)+1):nrow(comp)] <- "Test" B. 特征查看 View(comp) str(comp) summary(comp) apply(comp,2,FUN=function(x) round(sum(x==''|is.na(x))/nrow(comp),4)) 拿到数据先别急着处理,让我们来过一遍,并且产生一些猜想。