res = kmeans.fit(dataset_2) results.append(res.score(dataset_2)) plt.plot(range(1,10),results) plt.xlabel('Num Clusters') plt.ylabel('score') plt.title('Elbow Curve') 1. 2. 3. 4. 5. 6. 7. 8. 9. 数据集链接: https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutori...
dataset_1=data.iloc[:,1:5]dataset_1.head(10) 代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 results=[]foriinrange(1,10):kmeans=KMeans(n_clusters=i,init='k-means++')res=kmeans.fit(dataset_1)results.append(res.score(dataset_1))plt....
purchase_dict={}# interesting pointsforiintqdm(df.index):cust_id=df.at[i,'customer_id']art_i...
1，做有Reward那种，且仍然active的比赛。这类比赛牛人参加会比较多，竞争比较激烈，分数会计入ranking。d...
This project uses Kaggle’s mall customer dataset. You will use this data to perform data exploration, import essential packages, and gain insights about the data using R. You will also perform data visualization with R to identify the minimum and maximum customer ages, annual income, and spend...
res = kmeans.fit(dataset_2) results.append(res.score(dataset_2)) plt.plot(range(1,10),results) plt.xlabel('Num Clusters') plt.ylabel('score') plt.title('Elbow Curve') 1. 2. 3. 4. 5. 6. 7. 8. 9. 数据集链接: https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutori...
data=pd.read_csv('../input/Mall_Customers.csv')data.head() 代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 X=data.iloc[:,[3,4]].values# 将年度收入和支出分数作为特征 求最优聚类数 代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 ...
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Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Unexpected end of JSON input SyntaxError: Unexpected end of JSON input