ML with Python (IBM DS)

import random import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.datasets.samples_generator import make_blobs %matplotlib inline

np.random.seed(0)

X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)

plt.scatter(X[:, 0], X[:, 1], marker='.')

k_means = KMeans(init = "k-means++", n_clusters = 4, n_init = 12)

k_means.fit(X)

k_means_labels = k_means.labels_ k_means_labels

k_means_cluster_centers = k_means.cluster_centers_ k_means_cluster_centers

# Initialize the plot with the specified dimensions. fig = plt.figure(figsize=(10, 6)) # Colors uses a color map, which will produce an array of colors based on # the number of labels there are. We use set(k_means_labels) to get the # unique labels. colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels)))) # Create a plot ax = fig.add_subplot(1, 1, 1) # For loop that plots the data points and centroids. # k will range from 0-3, which will match the possible clusters that each # data point is in. for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors): # Create a list of all data points, where the data poitns that are # in the cluster (ex. cluster 0) are labeled as true, else they are # labeled as false. my_members = (k_means_labels == k) # Define the centroid, or cluster center. cluster_center = k_means_cluster_centers[k] # Plots the datapoints with color col. ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.') # Plots the centroids with specified color, but with a darker outline ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6) # Title of the plot ax.set_title('KMeans') # Remove x-axis ticks ax.set_xticks(()) # Remove y-axis ticks ax.set_yticks(()) # Show the plot plt.show()

# write your code here k_means1 = KMeans(init = "k-means++", n_clusters = 3, n_init = 12) k_means1.fit(X) k_means1_labels = k_means1.labels_ k_means1_cluster_centers = k_means1.cluster_centers_ fig = plt.figure(figsize=(10, 6)) colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means1_labels)))) ax = fig.add_subplot(1, 1, 1) for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors): my_members = (k_means1_labels == k) cluster_center = k_means1_cluster_centers[k] ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.') ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6) plt.show()

!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv

import pandas as pd cust_df = pd.read_csv("Cust_Segmentation.csv") cust_df.head()

df = cust_df.drop('Address', axis=1) df.head()

from sklearn.preprocessing import StandardScaler X = df.values[:,1:] X = np.nan_to_num(X) Clus_dataSet = StandardScaler().fit_transform(X) Clus_dataSet

clusterNum = 3 k_means = KMeans(init = "k-means++", n_clusters = clusterNum, n_init = 12) k_means.fit(X) labels = k_means.labels_ print(labels)

df["Clus_km"] = labels df.head(5)

df.groupby('Clus_km').mean()

area = np.pi * ( X[:, 1])**2 plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5) plt.xlabel('Age', fontsize=18) plt.ylabel('Income', fontsize=16) plt.show()

from mpl_toolkits.mplot3d import Axes3D fig = plt.figure(1, figsize=(8, 6)) plt.clf() ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134) plt.cla() # plt.ylabel('Age', fontsize=18) # plt.xlabel('Income', fontsize=16) # plt.zlabel('Education', fontsize=16) ax.set_xlabel('Education') ax.set_ylabel('Age') ax.set_zlabel('Income') ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))