Breaking News: Grepper is joining You.com. Read the official announcement!

kmeans python

Innocent Iguana answered on December 24, 2023 Popularity 8/10 Helpfulness 4/10

answer kmeans python

related kmeans python

kmeans python

Comment

# Scaling, normalization or standardization
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
samples = df.drop("target",axis=1)
scaler.fit(samples)
StandardScaler(copy=True, with_mean=True, with_std=True)
samples_scaled = scaler.transform(samples)

from sklearn.cluster import KMeans
model = KMeans(init="random", n_clusters=3, n_init=5, max_iter=100)
model.fit(samples)
labels = model.predict(samples)
#### labels = model.fit_predict(samples) ### Alternative
# See Distribution of samples in a cluster (how well the model performed)
ct = pd.crosstab(df['predicted_labels'], df['target'])
print(ct)
# Cluster quality, Inertia : the spread of clusters
print(model.inertia_)
# Locations of the centroids (You can plot with scatterplot)
centroids = model.cluster_centers_
# Iteration of convergence
print(model.n_iter_)
# Visualize for best cluster : Choose elbow as the optimized k
plt.plot(k_values, inertia_values, marker='o')
plt.show()

### k-means withPipeline
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.pipeline import make_pipeline
scaler = StandardScaler()
kmeans = KMeans(n_clusters=3)
pipeline = make_pipeline(scaler, kmeans)
pipeline.fit(samples)
labels = pipeline.predict(samples)

Popularity 8/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: k-means python

Link to this answer
Share Copy Link

Contributed on Dec 24 2023

Innocent Iguana

0 Answers Avg Quality 2/10

Closely Related Answers

kmeans python

Comment

# Import necessary libraries
from scipy.cluster.vq import kmeans, vq
import seaborn as sns
import matplotlib.pyplot as plt
# Make sure the columns you are working with are in "float" datatype
# Otherwise scipy generates an error while doing kmeans
df1['x'] = df1['x'].astype(float)
df1['y'] = df1['y'].astype(float)
# Step1 : Compute cluster centers (centroids) and the overall mean of squared distance of datapoints from the centers (distorion)
centroids, overall_distortion = kmeans(df1[['x', 'y']], 2)

# Step 2 : Predict the labels of clusters and the squared distance of datapoints from the centers (distorion)
df1['cluster_labels'], distortion_list = vq(df1[['x', 'y']], centroids)

# Plot the points with seaborn
sns.scatterplot(x='x', y='y', hue='cluster_labels', data=df1)
plt.show()

# Find best k through elbow plot: k on X-axis and overall_distortion on Y-axis
plt.plot(x=k_list, y=overall_distortion_list)
plt.show()

Popularity 8/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: python k-means pyth

Link to this answer
Share Copy Link

Contributed on Dec 31 2023

Innocent Iguana

0 Answers Avg Quality 2/10

kmeans python

Contents

More Related Answers

kmeans python

Closely Related Answers

kmeans python

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.