机器学习-DBSCAN聚类算法

在 上张贴 由 孔 祥博机器学习-DBSCAN聚类算法已关闭评论

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import pandas as pd
import numpy as np
beer=pd.read_csv('data.txt',sep=' ')
# print(beer)
X=beer[["calories","sodium","alcohol","cost"]]
# print(X)
from sklearn.cluster import KMeans
km=KMeans(n_clusters=3).fit(X)
km2=KMeans(n_clusters=2).fit(X)
# print(km.labels_)
beer["cluster"]=km.labels_
beer["cluster2"]=km2.labels_
# print(beer.sort_values('cluster'))
# print(beer.groupby("cluster").mean().reset_index())
import matplotlib.pyplot as plt
colors=np.array(['red','green','blue','yellow'])
# plt.scatter(beer['calories'],beer["alcohol"],c=colors[beer["cluster"]])
# plt.xlabel('Calories')
# plt.ylabel('Alcohol')
# plt.show()
# pd.scatter_matrix(beer[["calories","sodium","alcohol","cost"]],c=colors[beer["cluster"]], figsize=(10, 6))
# plt.show()
# Scaled data
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
X_scaled=scaler.fit_transform(X)
# print(X_scaled)
km3=KMeans(n_clusters=3).fit(X_scaled)
beer["scale_cluster"]=km3.labels_
# print(beer.sort_values('scale_cluster'))
from sklearn import metrics
score_scaled=metrics.silhouette_score(X,beer.scale_cluster)
score=metrics.silhouette_score(X,beer.cluster)
# print(score_scaled,score)
scores=[]
for k in range(2,20):
    labels=KMeans(n_clusters=k).fit(X).labels_
    score=metrics.silhouette_score(X,labels)
    scores.append(score)
 
plt.plot(list(range(2,20)),scores)
plt.xlabel("Number of Clusters Initialized")
plt.ylabel("Sihouette Score")
plt.xticks(np.arange(2,20,1))
plt.show()
 
from sklearn.cluster import DBSCAN
db=DBSCAN(eps=10,min_samples=2).fit(X)
labels=db.labels_
beer['cluster_db']=labels
beer.sort_values('cluster_db')
print(beer)

 

贷款申请最大化利润

在 上张贴 由 孔 祥博贷款申请最大化利润已关闭评论

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import pandas as pd
loans_2007 = pd.read_csv('LoanStats3a.csv', skiprows=1)
half_count = len(loans_2007) / 2
loans_2007 = loans_2007.dropna(thresh=half_count, axis=1)
loans_2007 = loans_2007.drop(['desc', 'url'],axis=1)
loans_2007.to_csv('loans_2007.csv', index=False)

[crayon-61a9b7f2a63f68…

机器学习-神经网络基础

在 上张贴 由 孔 祥博机器学习-神经网络基础已关闭评论

使用的是老生常谈的鸢尾花数据集

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import pandas
import matplotlib.pyplot as plt
import numpy as np
 
# Read in dataset
iris = pandas.read_csv("iris.csv")
 
# shuffle rows
shuffled_rows = np.random.permutation(iris.index)
iris = iris.loc[shuffled_rows,:]
 
print(iris.head())
 
# There are 2 species
print(iris.species.unique())
iris.hist()
plt.show()

[crayo…