def kmeans():
# Cargamos los datos con panda
dataset_red = pd.read_csv("winequality-red.csv")
dataset_white = pd.read_csv("winequality-white.csv")
dataset = pd.concat([dataset_red, dataset_white])
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, :1].values
# Metodo del codo para averiguar el numero de clusters
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i,
init="k-means++",
max_iter=400,
n_init=10,
random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title("Metodo de codo")
plt.xlabel("Numero de clusteres")
plt.ylabel("WCSS(k)")
plt.show()
# Aplicar metodo KMEANS para segmentar el dataset
kmeans = KMeans(n_clusters=3,
init="k-means++",
max_iter=400,
random_state=0)
y_kmeans = kmeans.fit_predict(X)
state = kmeans.__getstate__()
# Vizualizacion de los clusters
plt.scatter(X[y_kmeans == 0, 0],
X[y_kmeans == 0, 1],
s=100,
c="red",
label="cluster 1")
plt.scatter(X[y_kmeans == 1, 0],
X[y_kmeans == 1, 1],
s=100,
c="blue",
label="cluster 2")
plt.scatter(X[y_kmeans == 2, 0],
X[y_kmeans == 2, 1],
s=100,
c="green",
label="cluster 3")
plt.scatter(kmeans.cluster_centers_[:, 0],
kmeans.cluster_centers_[:, 1],
s=100,
c="yellow",
label="Baricentro")
plt.title("Cluster de vinos")
plt.xlabel("Total de azucar (g)")
plt.ylabel("Dioxido de sulfuro")
plt.legend()
plt.show()
# Precision
x_pred = X[y_kmeans, 0]
y_pred = X[y_kmeans, 1]
print("\nPrecision: {}%".format(
(metrics.completeness_score(X[y_kmeans, 0], X[y_kmeans, 1])) * 100))
