對于該數據先采用回歸算法，再轉為分類算法，這次使用聚類算法

聚類算法（無監督機器學習算法）

df = pd.read_csv("../input/Admission_Predict.csv",sep = ",") df=df.rename(columns = {'Chance of Admit ':'ChanceOfAdmit'}) serial = df["Serial No."] df.drop(["Serial No."],axis=1,inplace = True) df = (df- np.min(df))/(np.max(df)-np.min(df)) y = df.ChanceOfAdmit x = df.drop(["ChanceOfAdmit"],axis=1)

所有特征（x）均采用主成分分析法收集在一個特征中。

PCA

# for data visualization from sklearn.decomposition import PCA pca = PCA(n_components = 1, whiten= True ) # whitten = normalize pca.fit(x) x_pca = pca.transform(x) x_pca = x_pca.reshape(400,) dictionary = {"x":x_pca,"y":y} data = pd.DataFrame(dictionary) print("data:") print(data.head()) print("\ndf:") print(df.head())

K-means

利用肘形法確定K均值聚類的最佳聚類數是3

df["Serial No."] = serial from sklearn.cluster import KMeans wcss = [] for k in range(1,15):kmeans = KMeans(n_clusters=k)kmeans.fit(x)wcss.append(kmeans.inertia_) plt.plot(range(1,15),wcss) plt.xlabel("k values") plt.ylabel("WCSS") plt.show()kmeans = KMeans(n_clusters=3) clusters_knn = kmeans.fit_predict(x)df["label_kmeans"] = clusters_knnplt.scatter(df[df.label_kmeans == 0 ]["Serial No."],df[df.label_kmeans == 0].ChanceOfAdmit,color = "red") plt.scatter(df[df.label_kmeans == 1 ]["Serial No."],df[df.label_kmeans == 1].ChanceOfAdmit,color = "blue") plt.scatter(df[df.label_kmeans == 2 ]["Serial No."],df[df.label_kmeans == 2].ChanceOfAdmit,color = "green") plt.title("K-means Clustering") plt.xlabel("Candidates") plt.ylabel("Chance of Admit") plt.show()df["label_kmeans"] = clusters_knn plt.scatter(data.x[df.label_kmeans == 0 ],data[df.label_kmeans == 0].y,color = "red") plt.scatter(data.x[df.label_kmeans == 1 ],data[df.label_kmeans == 1].y,color = "blue") plt.scatter(data.x[df.label_kmeans == 2 ],data[df.label_kmeans == 2].y,color = "green") plt.title("K-means Clustering") plt.xlabel("X") plt.ylabel("Chance of Admit") plt.show()

層次聚類

采用樹形圖方法確定了層次聚類的最佳聚類數又是3

df["Serial No."] = serialfrom scipy.cluster.hierarchy import linkage, dendrogram merg = linkage(x,method="ward") dendrogram(merg,leaf_rotation = 90) plt.xlabel("data points") plt.ylabel("euclidean distance") plt.show()from sklearn.cluster import AgglomerativeClustering hiyerartical_cluster = AgglomerativeClustering(n_clusters = 3,affinity= "euclidean",linkage = "ward") clusters_hiyerartical = hiyerartical_cluster.fit_predict(x)df["label_hiyerartical"] = clusters_hiyerarticalplt.scatter(df[df.label_hiyerartical == 0 ]["Serial No."],df[df.label_hiyerartical == 0].ChanceOfAdmit,color = "red") plt.scatter(df[df.label_hiyerartical == 1 ]["Serial No."],df[df.label_hiyerartical == 1].ChanceOfAdmit,color = "blue") plt.scatter(df[df.label_hiyerartical == 2 ]["Serial No."],df[df.label_hiyerartical == 2].ChanceOfAdmit,color = "green") plt.title("Hierarchical Clustering") plt.xlabel("Candidates") plt.ylabel("Chance of Admit") plt.show()plt.scatter(data[df.label_hiyerartical == 0 ].x,data.y[df.label_hiyerartical == 0],color = "red") plt.scatter(data[df.label_hiyerartical == 1 ].x,data.y[df.label_hiyerartical == 1],color = "blue") plt.scatter(data[df.label_hiyerartical == 2 ].x,data.y[df.label_hiyerartical == 2],color = "green") plt.title("Hierarchical Clustering") plt.xlabel("X") plt.ylabel("Chance of Admit") plt.show()

聚類算法比較

k-均值聚類和層次聚類是相似的。

總結

• 任何一個數據都可以進行回歸，分類，甚至聚類的算法，關鍵如何處理數據采用不同種類的算法

總結

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